0. Project Setup¶
0.1 Packages & Device¶
# Torch
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader, Subset, random_split
from tqdm import tqdm
# Augmentation
import albumentations as A
from albumentations.pytorch import ToTensorV2
import scipy.io as sio
# Visualize Result
from sklearn.metrics import (confusion_matrix, accuracy_score,
precision_score, recall_score,
f1_score, roc_auc_score,
roc_curve, auc, precision_recall_curve,
average_precision_score)
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.preprocessing import label_binarize
# Basic
import numpy as np
import cv2
import os
import time
from typing import List, Tuple, Union
import random
import itertools
import copy
E:\Courses\CISC3024-Pattern-Recognition\cisc3024_pr_venv\lib\site-packages\albumentations\__init__.py:13: UserWarning: A new version of Albumentations is available: 1.4.21 (you have 1.4.18). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. check_for_updates()
device_name = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device_name)
print(f"Using device: {device_name}")
Using device: cuda
0.2 Global Configurations¶
path_dataset = "./data/SVHN_mat"
1. Data Processing and Augmentation¶
1.1 Download Datasets¶
Define dataset class, retrieve dataset.
1.1 Notes¶
_dat = sio.loadmat(os.path.join(path_dataset, "train_32x32.mat"))
# _dat['X'][0][0][0]
# np.array(_dat).shape
dat = np.transpose(_dat['X'], (3, 0, 1, 2))
# dat = dat.astype(np.float32)
dat.shape
# First Image
dat[0].shape
# First Row of Image
dat[0][0]
# First Pixel of Image
dat[0][0][0]
_transform = A.Compose([
A.Normalize(mean=[0.4376845359802246, 0.4437684714794159, 0.47280389070510864], std=[0.19803018867969513, 0.2010156661272049, 0.19703581929206848]),
ToTensorV2()
])
_img = dat[0]
_img = _transform(image=_img)['image']
# print(_img)
_img.shape
1.2 Dataset¶
class SVHNDataset(Dataset):
def __init__(self, mat_file, transform=None):
data = sio.loadmat(mat_file)
self.images = np.transpose(data['X'], (3, 0, 1, 2))
self.labels = data['y'].flatten()
self.labels[self.labels == 10] = 0
self.transform = transform # Allow postponed injection of transform.
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
image = self.images[idx]
label = self.labels[idx]
# There should always be a transform.
# It converts image to float, and permutes it from (32, 32, 3) to Tensor([3, 32, 32]).
# ...which is important!!
if self.transform is None:
raise ValueError("CISC3024 Custom Error: The transform should not be None when this object is passed into a DataLoader.")
image = self.transform(image=image)['image']
return image, label
def get_meanstd(self, bias=None):
if bias is not None:
random_bias = random.randint(0, bias)
images_ = []
for i in range(len(self.images)):
image = self.images[i]
image = image.astype(np.int16)
image = (image + random_bias) % 256
image = image.astype(np.uint8)
images_.append(image)
images_ = np.array(images_)
else:
images_ = self.images
images_ = images_.astype(np.float32) / 255.0
mean = np.mean(images_, axis=(0,1,2))
std = np.std(images_, axis=(0,1,2), ddof=0)
return mean.tolist(), std.tolist()
def overwrite(self, indices:Union[list, np.ndarray]):
"""
Create a deep copy of the mother dataset instance and only keep the wanted
data samples, controlled by indices.
"""
if any(index < 0 or index >= len(self.labels) for index in indices):
raise IndexError("CISC3024 Custom Error: One or more indices are out of bounds.")
new_dataset = copy.deepcopy(self)
new_dataset.images = self.images[indices]
new_dataset.labels = self.labels[indices]
return new_dataset
1.2 Peak A Data¶
def peek(dataset, index=None):
def unnormalize(img, mean, std):
"""Revert the normalization for visualization."""
img = img * std + mean
return np.clip(img, 0, 1)
mean, std = dataset.get_meanstd()
# Plotting multiple images in a grid
grid_rows, grid_cols = 1, 6
fig, axes = plt.subplots(grid_rows, grid_cols, figsize=(6, 6))
peek_index = random.randint(0, dataset.__len__()-1) if index is None else index
for i in range(grid_cols):
img_tensor, label = dataset.__getitem__(peek_index)
img = img_tensor.permute(1, 2, 0).numpy() # Convert to (H, W, C)
img = unnormalize(img, mean, std)
ax = axes[i] # Get subplot axis
ax.imshow(img)
ax.set_title(f"Label: {label}")
plt.tight_layout()
plt.show()
print(f"Peeking data from training set of index {peek_index}.\nImage Tnesor Size:{dataset.__getitem__(peek_index)[0].shape}")
2. Neural Network¶
2.1 Model Structure¶
class SmallVGG(nn.Module):
def __init__(self, frame_size=32):
super(SmallVGG, self).__init__()
self.frame_size = frame_size
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 8, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(8, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), # 16x16
nn.Conv2d(16, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), # 8x8
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2), # 4x4
)
self.fc_layers = nn.Sequential(
nn.Linear(frame_size * 4 * 4, 256),
nn.ReLU(),
nn.Linear(256, 10)
)
def forward(self, x):
x = self.conv_layers(x)
x = x.view(x.size(0), -1)
x = self.fc_layers(x)
return x
2.2 Train and Evaluate Function¶
def train_and_evaluate(model,
train_loader,
valid_loader,
criterion,
optimizer,
num_epochs=100,
stop_early_params=None):
# Record Losses to plot
train_losses = []
valid_losses = []
# Early stop params
current_optimized_model = None
current_min_valid_loss = np.inf
num_overfit_epochs = 0
for epoch in range(num_epochs):
# Train
model.train()
running_loss = 0.0
for images, labels in tqdm(train_loader):
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * len(images)
train_losses.append(running_loss / len(train_loader))
# Evaluate
model.eval()
valid_loss = 0.0
with torch.no_grad():
for images, labels in valid_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
valid_loss += loss.item() * len(images)
valid_losses.append(valid_loss / len(valid_loader))
print(f"Epoch[{epoch+1}/{num_epochs}], Train Loss:{train_losses[-1]:.4f}, Validation Loss:{valid_losses[-1]:.4f}")
# Early Stop?
if stop_early_params is None:
continue
if current_min_valid_loss - stop_early_params["min_delta"] > valid_losses[-1]: # Validation loss decreases
current_min_valid_loss = valid_losses[-1]
current_optimized_model = copy.deepcopy(model)
num_overfit_epochs = (num_overfit_epochs - 1) if num_overfit_epochs > 0 else 0
else: # Validation loss increases
num_overfit_epochs += 1
if num_overfit_epochs > stop_early_params["patience"]:
print(f"Early stopping at epoch {epoch+1}.")
model = current_optimized_model
break
return train_losses, valid_losses
2.3 Get Predictions¶
Multiple functions are defined to evaluate data. Below is a list of them.
def get_predictions(model_path, extra_loader):
if not isinstance(model_path, str):
model_state = model_path
else:
model_state = torch.load(model_path)
model = SmallVGG()
model.load_state_dict(model_state)
model.to(device)
model.eval()
pred_scores = [] # Prob. of predictions
true_labels = [] # Ground Truth
pred_labels = [] # Label of prediction, i.e., argmax(softmax(pred_scores))
with torch.no_grad():
for images, labels in tqdm(extra_loader):
images, labels = images.to(device), labels.to(device)
outputs = model(images)
pred_scores_batch = nn.functional.softmax(outputs, dim=-1)
pred_scores.extend(pred_scores_batch.cpu().tolist())
pred_labels.extend(outputs.argmax(dim=1).tolist())
true_labels.extend(labels.cpu().tolist())
return pred_scores, true_labels, pred_labels
2.4 Get Metrics¶
def get_metrics(true_labels, pred_labels):
accuracy = accuracy_score(true_labels, pred_labels)
precision = precision_score(true_labels, pred_labels, zero_division=1, average=None, labels=range(0,10))
recall = recall_score(true_labels, pred_labels, zero_division=1, average=None, labels=range(0,10))
f1 = f1_score(true_labels, pred_labels, zero_division=0, average=None, labels=range(0,10))
return accuracy, precision, recall, f1
def print_metrics(accuracies, f1s):
print(f"Accuracies:")
for acc in accuracies:
print(f"{acc:.3f}", end=" ")
print("\n")
print(f"F1 Score Lists:")
mean_f1s = []
for f1 in f1s:
for val in f1:
print(f"{val:.3f}", end=" ")
mean_f1 = np.mean(f1)
std_f1 = np.std(f1)
mean_f1s.append(mean_f1)
print(f"| Avg F1={mean_f1:.3f}, Std F1={std_f1}")
print(f"Best: {np.argmax(mean_f1s)+1}-th")
# Compute ROC AUC for each class
def get_roc_auc(true_labels_bin, pred_labels_bin):
roc_auc = dict()
for i in range(0, 10):
roc_auc[i] = roc_auc_score(true_labels_bin[:,i], np.array(pred_scores)[:, i])
return roc_auc
3. Experiments¶
3.0 Preparation¶
3.0.1 Plot Functions¶
The experiments will be a list of the following structures:
{
"HYPER_PARAM_1": combo[0],
"HYPER_PARAM_2": combo[1],
"train_losses": train_losses,
"valid_losses": valid_losses,
"model_state_dict": exp_model.state_dict()
}
Epoch-Loss Curves¶
def plot_el(loaded_experiments, hyper_param_names, n_rows=4, n_cols=4):
fig_size = (n_cols * 5, n_rows * 5)
n1, n2 = hyper_param_names
fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=fig_size)
# plt.tight_layout()
for i, ax in enumerate(axes.flat):
train_losses, valid_losses = loaded_experiments[i]["train_losses"], loaded_experiments[i]["valid_losses"]
ax.plot(train_losses, label=f"TRL, min={np.min(train_losses):.3f}")
ax.plot(valid_losses, label=f"VAL, min={np.min(valid_losses):.3f} at step={np.argmin(valid_losses)}")
ax.set_xlabel("Epochs")
ax.set_ylabel("Loss")
ax.set_title(f"{n1}={loaded_experiments[i][n1]}, {n2}={loaded_experiments[i][n2]}")
ax.legend(loc="upper right")
plt.show()
Get Experiment Results¶
def get_experiment_results(loaded_experiments, test_hyperparam_names, extra_loader):
experiment_results = []
n1, n2 = test_hyperparam_names
for i, exp in enumerate(loaded_experiments):
pred_scores, true_labels, pred_labels = get_predictions(exp['model_state_dict'], extra_loader)
experiment_results.append({
n1: exp[n1],
n2: exp[n2],
"true_labels": true_labels,
"pred_labels": pred_labels,
"pred_scores": pred_scores
})
print(f"First 10 true labels:")
[print(num, end=" ") for num in true_labels[:10]]
print(f"...\n")
print(f"First 10 pred labels:")
[print(num, end=" ") for num in pred_labels[:10]]
print(f"...\n")
print(f"First 5 pred_scores:")
[print(num, end=" ") for num in pred_scores[:5]]
print(f"...\n")
# del pred_scores, true_labels, pred_lables
torch.cuda.empty_cache()
return experiment_results
Confusion Matrix¶
def plot_cm(experiment_results, hyper_param_names, n_rows=4, n_cols=4):
fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 5, n_rows * 5))
axes = axes.flatten()
hparam_1, hparam_2 = hyper_param_names
for i, exp_rs in enumerate(experiment_results):
true_labels, pred_labels = exp_rs['true_labels'], exp_rs['pred_labels']
cm = confusion_matrix(true_labels, pred_labels)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=range(0,10))
disp.plot(ax=axes[i], cmap = plt.cm.Blues)
axes[i].set_title(f"Exp {i+1}: {hparam_1}={exp_rs[hparam_1]}, {hparam_2}={exp_rs[hparam_2]}")
plt.tight_layout()
plt.show()
Precision-Recall Curve¶
def plot_pr(experiment_results, hyper_param_names, n_rows=4, n_cols=4):
fig, axes = plt.subplots(n_rows,n_cols, figsize=(n_cols * 5, n_rows * 5))
axes = axes.flatten()
hparam_1, hparam_2 = hyper_param_names
accuracies = []
f1_scores = []
for i, exp_rs in enumerate(experiment_results):
true_labels, pred_labels, pred_scores = exp_rs['true_labels'], exp_rs['pred_labels'], exp_rs['pred_scores']
true_labels_bin, pred_labels_bin = label_binarize(true_labels, classes=range(0,10)), label_binarize(pred_labels, classes=range(0,10))
accuracy, precision, recall, f1 = get_metrics(true_labels, pred_labels)
accuracies.append(accuracy)
f1_scores.append(f1)
for j in range(0, 10):
# print(f"Class {j}: Prec:{precision[j]:.2f}, Recall:{recall[j]:.2f}, F_1 Score:{f1[j]:.2f}")
precision_i, recall_i, _ = precision_recall_curve(true_labels_bin[:, j], np.array(pred_scores)[:, j])
average_precision = average_precision_score(true_labels_bin[:, j], np.array(pred_scores)[:, j])
axes[i].step(recall_i, precision_i, where="post", label=f"Class {j} AP={average_precision:.2f}")
axes[i].set_title(f"PR-Curve {hparam_1}={exp_rs[hparam_1]}, {hparam_2}={exp_rs[hparam_2]}")
axes[i].legend()
axes[i].set_xlabel("Recall")
axes[i].set_ylabel("Precision")
# for j in range(i+1, 16):
# fig.delaxes(axes[j])
plt.tight_layout()
plt.show()
return accuracies, f1_scores
ROC-AUC Curve¶
def plot_rocauc(experiment_results, hyper_param_names, curve_type, n_rows=4, n_cols=4):
fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 5, n_rows * 5))
axes = axes.flatten()
hparam_1, hparam_2 = hyper_param_names
for i, exp_rs in enumerate(experiment_results):
true_labels, pred_scores = exp_rs['true_labels'], exp_rs['pred_scores']
true_labels_bin = label_binarize(true_labels, classes=range(0, 10))
# All Classes' ROC curve & ROC Area Under Curve
fpr = dict()
tpr = dict()
roc_auc = dict()
for j in range(10):
fpr[j], tpr[j], _ = roc_curve(true_labels_bin[:, j], np.array(pred_scores)[:, j])
roc_auc[j] = auc(fpr[j], tpr[j])
# Macro-Average ROC & ROC-AUC
all_fpr = np.unique(np.concatenate([fpr[j] for j in range(10)]))
mean_tpr = np.zeros_like(all_fpr)
for j in range(10):
mean_tpr += np.interp(all_fpr, fpr[j], tpr[j])
mean_tpr /= 10
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(true_labels_bin.ravel(), np.array(pred_scores).ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
# Plot only Macro or Micro ROC curves
if curve_type == "macro_micro":
axes[i].plot(fpr["macro"], tpr["macro"], label=f"Macro (AUC={roc_auc['macro']:.2f})")
axes[i].plot(fpr["micro"], tpr["micro"], label=f"Micro (AUC={roc_auc['micro']:.2f})")
elif curve_type == "all":
# Plot all ROC curves
for j in range(10):
axes[i].plot(fpr[j], tpr[j], label=f"Class {j} (AUC={roc_auc[j]:.2f})")
axes[i].plot([0, 1], [0, 1], "k--")
axes[i].set_xlabel("False Positive Rate")
axes[i].set_ylabel("True Positive Rate")
axes[i].set_title(f"ROC Curve {i+1}, {hparam_1}={exp_rs[hparam_1]}, {hparam_2}={exp_rs[hparam_2]}")
axes[i].legend(loc='lower right')
plt.tight_layout()
plt.show()
3.0.1 Datasets¶
def split_train_valid(train_dataset, train_ratio):
ori_len = len(train_dataset)
train_size = int(train_ratio * ori_len)
valid_size = ori_len - train_size
# These are subsets!! Don't directly use them or you will spend 2 hours solving for it.
train_subset, valid_subset = random_split(train_dataset, [train_size, valid_size])
# Re-construct two SVHNDataset object from indecies
train_dataset_ = train_dataset.overwrite(indices=train_subset.indices)
valid_dataset_ = train_dataset.overwrite(indices=valid_subset.indices)
return train_dataset_, valid_dataset_
3.1 Experiment 1: Optimizer¶
In the standard process of gradient descent, each update is proportional to the negative gradient (first-order derivative) of the loss function with respect to the parameter. In this traditional process, the learning rate is fixed, and it may cause problems.
- Oscillations. If locally, the learning rate is too high, the model will jump around the local minimum.
- Slow convergence. If locally, the learning rate is too low, the model will spend a lot of epochs to converge to a local minimum.
To solve this problem, we enable the learning rate to be adaptive by introducing the "momentum", a velocity-like term which accumulates past gradients in the direction of consistent descent.
- The velocity term is the weighted sum of previous gradients.
- ...such that the update direction does not only rely on the current gradient, but also on previous ones.
The update of velocity is represnted as: $$ v_t=\beta v_{t-1} + (1-\beta)\cdot\nabla J(\theta) $$ where $\beta$ is the momentum coefficient. In our experiments, $\beta$ will be fixed to $0.9$.
The update of parameters will be: $$ \theta_{t} = \theta_{t-1}-\eta\cdot v_{t} $$ In this experiment, we focus on the performance of different optimizers, each has its own optimized way to update the momentum. We will fix other variables, including transform, epoch number and learning rate, and only adjust the optimizers. There are a few optimizers to be chozen:
- Adaptive Moment Estimation (Adam)
- Stochastic Gradient Descent (SGD)
- Root Mean Square Propagation (RMSprop)
- Adam with Weight Decay (AdamW)
- Adaptive Gradient Algorithm (Adgrad)
- SGD with Momentum and Nesterove Accelerated Gradient
# Universal Train Dataset without splitting
exp1_universal_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"train_32x32.mat"))
# Train-Validation Split
exp1_train_dataset, exp1_valid_dataset = split_train_valid(exp1_universal_train_dataset, train_ratio=0.8)
if not isinstance(exp1_train_dataset, SVHNDataset) or not isinstance(exp1_valid_dataset, SVHNDataset):
raise TypeError("CISC3024 Custom Error: The dataset should be an instance of SVHNDataset.")
# Normalize
exp1_mean, exp1_std = exp1_train_dataset.get_meanstd()
exp1_hyperparams = {
"num_epochs": 25,
"lr": 1e-3,
"criterion": nn.CrossEntropyLoss(),
"transform": A.Compose([
A.Normalize(mean=exp1_mean, std=exp1_std),
ToTensorV2()
])
}
# Inject Transform
exp1_train_dataset.transform = exp1_hyperparams['transform']
exp1_valid_dataset.transform = exp1_hyperparams['transform']
# Test Dataset
exp1_test_dataset = SVHNDataset(mat_file=os.path.join(path_dataset, "test_32x32.mat"), transform=exp1_hyperparams['transform'])
del exp1_universal_train_dataset
print(f"Training Size:{exp1_train_dataset.__len__()}, Validation Size:{exp1_valid_dataset.__len__()}")
print(f"Channel Means: {exp1_mean}\nChannel Stds: {exp1_std}")
Training Size:58605, Validation Size:14652 Channel Means: [0.43772128224372864, 0.44378969073295593, 0.4728474020957947] Channel Stds: [0.19793079793453217, 0.20086902379989624, 0.1968136429786682]
Define changing & non-changing hyper parameters.
exp1_models = [SmallVGG().to(device) for _ in range(0,6)]
candidate_optimizers = [
optim.Adam(exp1_models[0].parameters(), lr=exp1_hyperparams['lr']),
optim.SGD(exp1_models[1].parameters(), lr=exp1_hyperparams['lr'], momentum=0.9),
optim.RMSprop(exp1_models[2].parameters(), lr=exp1_hyperparams['lr']),
optim.AdamW(exp1_models[3].parameters(), lr=exp1_hyperparams['lr'], weight_decay=0.01),
optim.Adagrad(exp1_models[4].parameters(), lr=exp1_hyperparams['lr']),
optim.SGD(exp1_models[5].parameters(), lr=exp1_hyperparams['lr'], momentum=0.9, nesterov=True)]
for model in exp1_models:
print(id(model), end=", ")
3044296310112, 3043927082032, 3043927083520, 3043927084144, 3043927085008, 3044137279152,
Train, Validation and Test datasets.
Train, Validation and Test Data Loaders.
# Data Loaders
exp1_train_loader = DataLoader(exp1_train_dataset, batch_size=128, shuffle=True)
exp1_valid_loader = DataLoader(exp1_valid_dataset, batch_size=128, shuffle=True)
exp1_test_loader = DataLoader(exp1_test_dataset, batch_size=128, shuffle=False)
Run Experiments
def run_exp1(optimizers, models, hyper_params, train_loader, valid_loader):
experiments = []
for i, [optimizer, exp1_model] in enumerate(zip(optimizers, models)):
print(f"Experiment {i+1}. Running experiment on optimizer: {optimizer.__class__.__name__}")
criterion = hyper_params['criterion']
num_epochs = hyper_params['num_epochs']
train_losses, valid_losses = train_and_evaluate(exp1_model, train_loader, valid_loader, criterion, optimizer, num_epochs)
experiments.append({
"optimizer": optimizer.__class__.__name__,
"others":"same",
"train_losses": train_losses,
"valid_losses": valid_losses,
"model_state_dict": exp1_model.state_dict()
})
del exp1_model, criterion, optimizer
torch.cuda.empty_cache()
return experiments
exp1 = run_exp1(candidate_optimizers, exp1_models, exp1_hyperparams, exp1_train_loader, exp1_valid_loader)
time_str = str(time.time()).replace(".","")
torch.save(exp1, f"./models/exp1_{time_str}.pth")
Load Experiments
Load Experiment objects and plot results.
exp1_loaded = torch.load("./models/exp1_17305518422052872.pth")
exp1_results = get_experiment_results(exp1_loaded, test_hyperparam_names=["optimizer", "others"], extra_loader=exp1_test_loader)
plot_el(exp1_loaded, ["optimizer", "others"], n_rows=1, n_cols=6)
plot_cm(exp1_results, ["optimizer", "others"], n_rows=1, n_cols=6)
Precision-Recall Curve
Overfitted to inspect training performance. This "Best" does not tell the optimum optimizer.
exp1_accuracies, exp1_f1s = plot_pr(exp1_results, ["optimizer", "others"], n_rows=1, n_cols=6)
print_metrics(exp1_accuracies, exp1_f1s)
Accuracies: 0.907 0.196 0.906 0.901 0.735 0.196 F1 Score Lists: 0.912 0.935 0.938 0.874 0.921 0.905 0.881 0.906 0.847 0.867 | Avg F1=0.899, Std F1=0.028664446684648247 0.000 0.328 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 | Avg F1=0.033, Std F1=0.09827503131926378 0.888 0.933 0.939 0.876 0.913 0.903 0.878 0.923 0.850 0.865 | Avg F1=0.897, Std F1=0.028470034992916605 0.887 0.935 0.936 0.871 0.914 0.907 0.871 0.908 0.830 0.843 | Avg F1=0.890, Std F1=0.03434799781092271 0.723 0.840 0.802 0.638 0.776 0.700 0.647 0.789 0.515 0.628 | Avg F1=0.706, Std F1=0.09492988307421246 0.000 0.328 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 | Avg F1=0.033, Std F1=0.09827503131926378 Best: 1-th
plot_rocauc(exp1_results, ["optimizer", "others"], curve_type="all", n_rows=1, n_cols=6)
plot_rocauc(exp1_results, ["optimizer", "others"], curve_type="macro_micro", n_rows=1, n_cols=6)
3.2 Experiment 2: Epoch Number and Learning Rate¶
This experiment seeks to find the effect of different combinations of epoch numbers and learning rates on the training & testing performance of the neural network.
3.2.1 Experiment 2-1: Rough Search¶
In this sub-experiment, we perform a rough search on the epochs and learning rate. We promoted four possible values for both parameters: $$ \text{candidate epochs}=\{10, 15, 20, 25\} $$ $$ \text{candidate lr}=\{1.0\times 10^{-3},1.0\times 10^{-4},1.0\times 10^{-5},1.0\times 10^{-6}\} $$
# Universal Train Dataset without splitting
exp2_universal_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"train_32x32.mat"))
# Train & Validation Datasets
exp2_train_dataset, exp2_valid_dataset = split_train_valid(exp2_universal_train_dataset, train_ratio=0.8)
del exp2_universal_train_dataset # Unload the mill and kill the donkey
# Normalize
exp2_mean, exp2_std = exp2_train_dataset.get_meanstd()
exp2_hyperparams = {
"criterion": nn.CrossEntropyLoss(),
"transform": A.Compose([
A.Normalize(mean=exp2_mean, std=exp2_std),
ToTensorV2()
]),
"optimizer":optim.Adam,
}
exp2_train_dataset.transform = exp2_hyperparams['transform']
exp2_valid_dataset.transform = exp2_hyperparams['transform']
# Test Dataset
exp2_test_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"test_32x32.mat"), transform=exp2_hyperparams['transform'])
print(f"Training Size:{exp2_train_dataset.__len__()}, Validation Size:{exp2_valid_dataset.__len__()}")
print(f"Channel Means: {exp2_mean}\nChannel Stds: {exp2_std}")
Training Size:58605, Validation Size:14652 Channel Means: [0.4374935030937195, 0.44353100657463074, 0.4726291298866272] Channel Stds: [0.1980431079864502, 0.2009742110967636, 0.19704842567443848]
candidate_epochs = [10, 15, 20, 25]
candidate_lr = [1e-3, 1e-4, 1e-5, 1e-6]
exp2_train_loader = DataLoader(exp2_train_dataset, batch_size=128, shuffle=True)
exp2_valid_loader = DataLoader(exp2_valid_dataset, batch_size=128, shuffle=True)
exp2_test_loader = DataLoader(exp2_test_dataset, batch_size=128, shuffle=False)
def run_exp2_1(epochs, lr_list, hyper_params, train_loader, valid_loader):
combinations = list(itertools.product(epochs, lr_list))
experiments = []
for i, combo in enumerate(combinations):
num_epochs, lr = combo
print(f"Running Exp {i+1}: num_epoch={num_epochs}, lr={lr}")
this_model = SmallVGG().to(device)
criterion = hyper_params['criterion']
optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)
train_losses, valid_losses = train_and_evaluate(this_model, train_loader, valid_loader, criterion, optimizer, num_epochs)
experiments.append({
"num_epochs": num_epochs,
"lr": lr,
"train_losses": train_losses,
"valid_losses": valid_losses,
"model_state_dict": this_model.state_dict()
})
del this_model, criterion, optimizer
return experiments
exp2_1 = run_exp2_1(candidate_epochs, candidate_lr, exp2_hyperparams, exp2_train_loader, exp2_valid_loader)
time_str = str(time.time()).replace(".","")
torch.save(exp2_1, f"./models/exp2-1_{time_str}.pth")
exp2_1_loaded = torch.load("./models/exp2-1_17305539358378615.pth")
exp2_1_results = get_experiment_results(exp2_1_loaded, test_hyperparam_names=["num_epochs", "lr"], extra_loader=exp2_test_loader)
3.2.1-1 Epoch-Loss Curve¶
We found that the key to the training performance of a model is the learning rate. Epoch number only controls the progress of training.
From the perspective of learning rate (each column), only the learning rate of $1.0\times 10^{-3}$ shows a sign of convergence under each candidate epochs. With this learning rate, the model even overfitted under experiments with an epoch number over $15$. The best model we conclude from this rough selection is the one with the combination of $\text{num\_epoch}=10\land\text{lr}=1.0\times10^{-3}$. The minimum validation loss is $36.648$ at step $7$, which is the lowest of all $16$ samples. However, this doesn't mean that it is optimal since it may jump over a local minimum.
Moreover, as we inspect the performance on smaller learning rates, we found that they tend to converge in a way further epoch steps. Moreover, for the learning rate $1.0\times 10^{-6}$, the learning rate is too low that the model can not even fit under nearly-finite epochs.
plot_el(exp2_1_loaded, ["num_epochs", "lr"], n_rows=4, n_cols=4)
3.2.1-2 Confusion Matrix¶
In this rough search, the confusion matrix varies on different learning rates, and tends to be identical on different epochs.
Under a same epoch number, as leraning rate gets smaller, the confusion matrix gets "blurrer", meaning that the prediction is less accurate from the whole perspective. The learning rates under $1.0\times 10^{-5}$ are too low that the model can't converge in a reasonably number of epochs. For the lowest learning rate of $1.0\times 10^{-6}$, the model is not fitted at all. It classifies every number into 1, the number with the richest amount in the dataset.
plot_cm(exp2_1_results, ["num_epochs", "lr"], n_rows=4, n_cols=4)
3.2.1-3 Precision-Recall Curve¶
From a numerical perspective over the testing performance, the combination of $\text{num\_epoch}=15\land\text{lr}=1.0\times10^{-3}$ gives the highest accuracy of $0.907$, highest average $F_1$ score of $0.916$ and the lowest $F_1$ variance per-class of $0.025$.
exp2_1_accuracies, exp2_1_f1s = plot_pr(exp2_1_results, ["num_epochs", "lr"], n_rows=4, n_cols=4)
print_metrics(exp2_1_accuracies, exp2_1_f1s)
Accuracies: 0.917 0.825 0.336 0.196 0.912 0.865 0.471 0.196 0.907 0.878 0.663 0.196 0.908 0.880 0.713 0.196 F1 Score Lists: 0.918 0.942 0.947 0.889 0.925 0.913 0.900 0.931 0.855 0.868 | Avg F1=0.909, Std F1=0.029051096513454423 0.811 0.904 0.880 0.774 0.838 0.791 0.745 0.860 0.703 0.731 | Avg F1=0.804, Std F1=0.06335319369501424 0.040 0.570 0.378 0.189 0.273 0.087 0.002 0.237 0.026 0.004 | Avg F1=0.180, Std F1=0.17824165219527116 0.000 0.328 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 | Avg F1=0.033, Std F1=0.09827503131926378 0.914 0.934 0.945 0.882 0.932 0.896 0.878 0.919 0.863 0.875 | Avg F1=0.904, Std F1=0.02715514894076909 0.866 0.930 0.912 0.808 0.881 0.829 0.809 0.899 0.794 0.769 | Avg F1=0.850, Std F1=0.05240811428774516 0.154 0.743 0.509 0.321 0.550 0.430 0.122 0.550 0.005 0.080 | Avg F1=0.347, Std F1=0.23453887474256918 0.000 0.328 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 | Avg F1=0.033, Std F1=0.09827503131926378 0.916 0.926 0.937 0.875 0.922 0.905 0.879 0.919 0.858 0.863 | Avg F1=0.900, Std F1=0.027185167062975517 0.875 0.932 0.913 0.830 0.890 0.849 0.841 0.904 0.810 0.815 | Avg F1=0.866, Std F1=0.04073441033554439 0.596 0.770 0.749 0.632 0.679 0.614 0.618 0.699 0.294 0.526 | Avg F1=0.618, Std F1=0.1283615011641036 0.000 0.328 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 | Avg F1=0.033, Std F1=0.09827503131926378 0.904 0.943 0.926 0.881 0.914 0.913 0.876 0.921 0.863 0.850 | Avg F1=0.899, Std F1=0.02860358424237646 0.879 0.924 0.925 0.821 0.886 0.867 0.832 0.905 0.820 0.820 | Avg F1=0.868, Std F1=0.040548747485917386 0.689 0.842 0.773 0.646 0.711 0.672 0.640 0.763 0.460 0.549 | Avg F1=0.674, Std F1=0.10523037482252583 0.000 0.328 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 | Avg F1=0.033, Std F1=0.09827503131926378 Best: 1-th
plot_rocauc(exp2_1_results, ["num_epochs", "lr"], curve_type="all", n_rows=4, n_cols=4)
plot_rocauc(exp2_1_results, ["num_epochs", "lr"], curve_type="macro_micro", n_rows=4, n_cols=4)
3.2.2 Experiment 2-2: Detailed¶
Previous sub-experiment tells that the best combination from all the listed ones is $\text{num\_epoch}=15 \land \text{lr}=1.0\times 10^{-3}$.
This is a rough solution, as it may jump over local minimums. We want to find a better learning rate around $1.0\times 10^{-3}$, with an even more detailed distinction between candidate values, so that it may reveal a missing local minimum without using too much epochs.
We conducted an excessive experiment, purposely seeking an overfitting point over the listed candidate learning rates. We do this by setting the epoch number to $50$.
exp2_2_hyperparams = {
"num_epoch": 15,
"criterion": nn.CrossEntropyLoss(),
"transform": A.Compose([
A.Normalize(mean=exp2_mean, std=exp2_std),
ToTensorV2()
]),
"optimizer":optim.Adam,
}
# More detailed candidate learning rates around 1e-3, that is 10e-4.
exp2_2_candidate_lr = np.geomspace(1e-4, 5e-3, 8)
print(exp2_2_candidate_lr)
[0.0001 0.00017487 0.00030579 0.00053472 0.00093506 0.00163512 0.0028593 0.005 ]
def run_exp2_2(lr_list, hyper_params, train_loader, test_loader):
experiments = []
for i, lr in enumerate(lr_list):
print(f"Running Exp {i+1}: lr={lr}")
this_model = SmallVGG().to(device)
num_epochs = hyper_params['num_epoch']
criterion = hyper_params['criterion']
optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)
train_losses, valid_losses = train_and_evaluate(this_model, train_loader, test_loader, criterion, optimizer, num_epochs)
experiments.append({
"num_epochs": num_epochs,
"lr": lr,
"train_losses": train_losses,
"valid_losses": valid_losses,
"model_state_dict": this_model.state_dict()
})
del this_model, criterion, optimizer
return experiments
exp2_2 = run_exp2_2(exp2_2_candidate_lr, exp2_2_hyperparams, exp2_train_loader, exp2_valid_loader)
time_str = str(time.time()).replace(".", "")
torch.save(exp2_2, f"./models/exp2-2_{time_str}.pth")
exp2_2_loaded = torch.load("./models/exp2-2_17305693866477516.pth")
exp2_2_results = get_experiment_results(exp2_2_loaded, test_hyperparam_names=["num_epochs", "lr"], extra_loader=exp2_test_loader)
3.2.2-1 Epoch-Loss Curve¶
For timing issues, an epoch of $15$ is our tolerance line. By inspecting the epoch-loss curve, we found that all the experiments are nearly overfitted around the end of the epochs.
From all the detailed searches, the learning rate of 9.35e-4, yields the lowest validation loss of $38.227$ at step $7$, which is the overfitting point. This minimum is worse than the one produced by the learning rate 1e-3, which is $36.549$. Therefore, for the trade-off of time and performance, we choose the combination of epoch=$15$ and lr=$0.001$ for the following experiments.
plot_el(exp2_2_loaded, ["num_epochs", "lr"], n_rows=2, n_cols=4)
3.2.2-2 Confusion Matrix¶
At a glance, from the perspective of confusion matrix, the testing performance on unknown data is roughly identical.
plot_cm(exp2_2_results, ["num_epochs", "lr"], n_rows=2, n_cols=4)
3.2.2-3 Precision-Recall Curve¶
By inspecting the evaluation metrics, we found our judgement correct. From all the over-fitted model, the model with learning rate of $7.0\times 10^{-4}$ yields the highest accuracy of $0.909$ and the highest average per-class $F_1$ score of $0.903$. Besides, the per-class $F_1$ score is also less variant under the learning rate of $7.0\times 10^{-4}$, with the standard deviation of $0.023$.
exp2_2_accuracies, exp2_2_f1s = plot_pr(exp2_2_results, ["num_epochs", "lr"], n_rows=2, n_cols=4)
print_metrics(exp2_2_accuracies, exp2_2_f1s)
Accuracies: 0.873 0.885 0.905 0.906 0.912 0.903 0.893 0.882 F1 Score Lists: 0.873 0.929 0.912 0.822 0.888 0.851 0.829 0.901 0.795 0.798 | Avg F1=0.860, Std F1=0.04542290722465954 0.892 0.936 0.916 0.850 0.905 0.854 0.843 0.906 0.819 0.808 | Avg F1=0.873, Std F1=0.04146430928239567 0.908 0.942 0.941 0.874 0.921 0.888 0.857 0.924 0.840 0.839 | Avg F1=0.893, Std F1=0.03748405274950539 0.911 0.942 0.941 0.868 0.923 0.889 0.875 0.917 0.868 0.829 | Avg F1=0.896, Std F1=0.03485676831682514 0.913 0.936 0.943 0.887 0.928 0.912 0.875 0.926 0.852 0.875 | Avg F1=0.905, Std F1=0.029020749926052154 0.901 0.932 0.937 0.880 0.908 0.912 0.873 0.918 0.835 0.841 | Avg F1=0.894, Std F1=0.033859473636702976 0.887 0.926 0.934 0.862 0.900 0.882 0.854 0.915 0.829 0.836 | Avg F1=0.883, Std F1=0.035070141262265965 0.873 0.921 0.919 0.859 0.888 0.880 0.832 0.891 0.814 0.827 | Avg F1=0.870, Std F1=0.03530344586873121 Best: 5-th
3.2.2-4 ROC-AUC Curve¶
The ROC-AUC Curve under all the detailed candidate learning rates are roughly identical.
plot_rocauc(exp2_2_results, ["num_epochs", "lr"], curve_type="all", n_rows=2, n_cols=4)
plot_rocauc(exp2_2_results, ["num_epochs", "lr"], curve_type="macro_micro", n_rows=2, n_cols=4)
3.3 Experiment 3: Image Augmentation Parameters¶
3.3.1 Experiment 3-1: Rotation Angles and Crop Ratios¶
exp3_universal_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset, "train_32x32.mat"))
exp3_train_dataset, exp3_valid_dataset = split_train_valid(exp3_universal_train_dataset, train_ratio=0.8)
del exp3_universal_train_dataset
# The mean & std here will only be used for experiment 3-1.
exp3_1_mean, exp3_1_std = exp3_train_dataset.get_meanstd()
exp3_1_hyperparams = {
"num_epochs": 50,
"lr": 1e-3,
"criterion": nn.CrossEntropyLoss(),
"optimizer": optim.Adam,
"transform": A.Compose([
A.Normalize(mean=exp1_mean, std=exp1_std),
ToTensorV2()
])
}
# Transform of train dataset will be altered in the experiments.
exp3_valid_dataset.transform = exp3_1_hyperparams['transform']
exp3_test_dataset = SVHNDataset(mat_file=os.path.join(path_dataset, "test_32x32.mat"), transform=exp3_1_hyperparams["transform"])
print(f"Training Size:{exp3_train_dataset.__len__()}, Validation Size:{exp3_valid_dataset.__len__()}")
print(f"Channel Means: {exp3_1_mean}\nChannel Stds: {exp3_1_std}")
Training Size:58605, Validation Size:14652 Channel Means: [0.43759557604789734, 0.4438314437866211, 0.47290241718292236] Channel Stds: [0.19781899452209473, 0.2008139044046402, 0.19679588079452515]
# Group 1
candidate_angles = [15, 30, 45, 60]
candidate_crops = [0.08, 0.24, 0.40, 0.60] # Left Boundary
exp3_valid_loader = DataLoader(exp3_valid_dataset, batch_size=128, shuffle=False)
exp3_test_loader = DataLoader(exp3_test_dataset, batch_size=128, shuffle=False)
def run_exp3_1(angles, crops, hyper_params, train_dataset, valid_loader):
combinations = list(itertools.product(angles, crops))
experiments = []
for i, combo in enumerate(combinations):
angle, crop = combo
print(f"Running Exp {i+1}: angles={angle}, crop={crop}")
this_model = SmallVGG().to(device)
num_epochs = hyper_params['num_epochs']
lr = hyper_params['lr']
criterion = hyper_params['criterion']
optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)
# Define Transform
this_transform = A.Compose([
A.RandomResizedCrop(32, 32, scale=(crop, 1.0)),
A.Rotate(limit=angle),
A.Normalize(mean=exp3_1_mean, std=exp3_1_std),
ToTensorV2()
])
# Generate Dataset
print(f"Exp {i+1}: Generating dataset from transform")
train_dataset.transform = this_transform
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
# Train Model
train_losses, valid_losses = train_and_evaluate(this_model,
train_loader,
valid_loader,
criterion,
optimizer,
num_epochs,
stop_early_params={
"min_delta": 0.01,
"patience": 5
})
experiments.append({
"angle": angle,
"crop": crop,
"train_losses": train_losses,
"valid_losses": valid_losses,
"model_state_dict": this_model.state_dict()
})
del this_model, criterion, optimizer
torch.cuda.empty_cache()
return experiments
exp3_1 = run_exp3_1(candidate_angles, candidate_crops, exp3_1_hyperparams, exp3_train_dataset, exp3_valid_loader)
time_str = str(time.time()).replace(".","")
torch.save(exp3_1, f"./models/exp3-1_{time_str}.pth")
exp3_1_loaded = torch.load("./models/exp3-1_17306646089970913.pth")
exp3_1_results = get_experiment_results(exp3_1_loaded, test_hyperparam_names=["angle", "crop"], extra_loader=exp3_test_loader)
plot_el(exp3_1_loaded, ["angle", "crop"], n_rows=4, n_cols=4)
plot_cm(exp3_1_results, ["angle", "crop"], n_rows=4, n_cols=4)
exp3_accuracies, exp3_f1s = plot_pr(exp3_1_results, ["angle", "crop"], n_rows=4, n_cols=4)
print_metrics(exp3_accuracies, exp3_f1s)
Accuracies: 0.912 0.916 0.917 0.931 0.892 0.898 0.917 0.928 0.886 0.893 0.914 0.928 0.891 0.888 0.910 0.926 F1 Score Lists: 0.889 0.942 0.942 0.885 0.939 0.922 0.887 0.924 0.843 0.848 | Avg F1=0.902, Std F1=0.03538451976963852 0.895 0.940 0.939 0.894 0.939 0.923 0.894 0.925 0.862 0.864 | Avg F1=0.908, Std F1=0.02847237430596348 0.899 0.936 0.945 0.893 0.935 0.927 0.900 0.923 0.876 0.858 | Avg F1=0.909, Std F1=0.02722603637075925 0.915 0.949 0.952 0.906 0.948 0.937 0.925 0.930 0.892 0.890 | Avg F1=0.925, Std F1=0.02181626856531463 0.892 0.927 0.924 0.845 0.925 0.890 0.870 0.875 0.845 0.837 | Avg F1=0.883, Std F1=0.03265762724812469 0.893 0.934 0.930 0.874 0.925 0.905 0.846 0.901 0.808 0.847 | Avg F1=0.886, Std F1=0.03969946400782543 0.904 0.943 0.947 0.889 0.934 0.921 0.896 0.911 0.873 0.878 | Avg F1=0.910, Std F1=0.024999702435631828 0.918 0.946 0.952 0.903 0.946 0.927 0.908 0.926 0.892 0.900 | Avg F1=0.922, Std F1=0.020141128246637333 0.837 0.922 0.941 0.842 0.913 0.900 0.865 0.915 0.797 0.797 | Avg F1=0.873, Std F1=0.04999486924122103 0.892 0.926 0.933 0.848 0.918 0.893 0.862 0.899 0.829 0.842 | Avg F1=0.884, Std F1=0.035008693882825154 0.901 0.942 0.939 0.871 0.935 0.914 0.901 0.913 0.874 0.877 | Avg F1=0.907, Std F1=0.025555951863289215 0.921 0.944 0.954 0.900 0.950 0.930 0.921 0.922 0.890 0.882 | Avg F1=0.922, Std F1=0.02344422620633198 0.874 0.929 0.931 0.858 0.887 0.893 0.855 0.923 0.823 0.834 | Avg F1=0.881, Std F1=0.036647593322034946 0.857 0.926 0.923 0.847 0.916 0.904 0.882 0.905 0.809 0.796 | Avg F1=0.876, Std F1=0.0446762695421183 0.904 0.940 0.940 0.882 0.923 0.905 0.887 0.914 0.866 0.855 | Avg F1=0.902, Std F1=0.027712221334215276 0.904 0.949 0.955 0.892 0.948 0.912 0.916 0.922 0.893 0.894 | Avg F1=0.919, Std F1=0.023072110295838992 Best: 4-th
plot_rocauc(exp3_1_results, ["angle", "crop"], curve_type="all", n_rows=4, n_cols=4)
plot_rocauc(exp3_1_results, ["angle", "crop"], curve_type="macro_micro", n_rows=4, n_cols=4)
3.3.2 Experiment 3-2: Ratios & Bias¶
exp3_2_hyperparams = {
"num_epochs": 50,
"lr": 1e-3,
"criterion": nn.CrossEntropyLoss(),
"optimizer": optim.AdamW,
"crop":0.6,
"angle":15,
"transform": A.Compose([
A.Normalize(mean=exp1_mean, std=exp1_std),
ToTensorV2()
])
}
class AddBiasTransform:
def __init__(self, bias: Union[int, Tuple[int, int]]) -> None:
if isinstance(bias, tuple):
self.bias1 = bias[0]
self.bias2 = bias[1]
else:
self.bias1 = 0
self.bias2 = bias
def __call__(self, img: np.ndarray) -> np.ndarray:
_dtype = img.dtype
bias_value = random.randint(self.bias1, self.bias2)
img = (img.astype(np.int16) + bias_value) % 256
return img.astype(_dtype)
# Group 2
candidate_ratios = [0.25, 0.42, 0.58, 0.75]
candidate_channel_biases = [0, 32, 64, 128]
Control candidates for different variables
def run_exp3_2(ratios, biases, hyper_params, train_dataset, valid_dataset):
combinations = list(itertools.product(ratios, biases))
experiments = []
for i, combo in enumerate(combinations):
ratio, bias = combo
print(f"Running Exp {i+1}: ratio={ratio}, bias={bias}")
this_model = SmallVGG().to(device)
num_epochs = hyper_params['num_epochs']
lr = hyper_params['lr']
criterion = hyper_params['criterion']
optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)
# Define Transform
this_mean, this_std = train_dataset.get_meanstd(bias=bias)
this_train_transform = A.Compose([
A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
A.RandomResizedCrop(32, 32, scale=(hyper_params['crop'], 1.0), ratio=(ratio, 1.0 / ratio)),
A.Rotate(limit=hyper_params['angle']),
A.Normalize(mean=this_mean, std=this_std),
ToTensorV2()
])
this_valid_transform = A.Compose([
A.Normalize(mean=this_mean, std=this_std),
ToTensorV2()
])
# Generate Dataset
print(f"Exp {i+1}: Generating dataset from transform")
train_dataset.transform = this_train_transform
valid_dataset.transform = this_valid_transform
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=128, shuffle=False)
# Train Model
train_losses, valid_losses = train_and_evaluate(this_model,
train_loader,
valid_loader,
criterion,
optimizer,
num_epochs,
stop_early_params={
"min_delta": 0.01,
"patience": 5
})
experiments.append({
"ratio": ratio,
"bias": bias,
"train_losses": train_losses,
"valid_losses": valid_losses,
"model_state_dict": this_model.state_dict()
})
del this_model, criterion, optimizer
# del train_loader, valid_loader
torch.cuda.empty_cache()
return experiments
exp3_2 = run_exp3_2(candidate_ratios, candidate_channel_biases, exp3_2_hyperparams, exp3_train_dataset, exp3_valid_dataset)
time_str = str(time.time()).replace(".", "")
torch.save(exp3_2, f"./models/exp3-2_{time_str}.pth")
Running Exp 1: ratio=0.25, bias=0
D:\Temps\temp\ipykernel_22900\3883611529.py:17: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial(). A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
Exp 1: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.36it/s]
Epoch[1/50], Train Loss:251.9213, Validation Loss:120.0689
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Epoch[2/50], Train Loss:115.4026, Validation Loss:68.1095
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Epoch[3/50], Train Loss:83.2680, Validation Loss:53.0528
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Epoch[4/50], Train Loss:69.8547, Validation Loss:49.7772
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Epoch[5/50], Train Loss:63.2935, Validation Loss:45.3321
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Epoch[6/50], Train Loss:58.2844, Validation Loss:43.3364
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Epoch[7/50], Train Loss:54.9751, Validation Loss:41.4382
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Epoch[8/50], Train Loss:52.1351, Validation Loss:38.3597
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Epoch[9/50], Train Loss:50.2174, Validation Loss:38.7325
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Epoch[10/50], Train Loss:48.7687, Validation Loss:38.7515
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Epoch[11/50], Train Loss:46.6847, Validation Loss:38.8695
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Epoch[12/50], Train Loss:46.2468, Validation Loss:36.7131
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Epoch[13/50], Train Loss:44.5507, Validation Loss:36.0958
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.20it/s]
Epoch[14/50], Train Loss:43.8005, Validation Loss:35.5141
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Epoch[15/50], Train Loss:43.4674, Validation Loss:35.1157
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Epoch[16/50], Train Loss:42.0704, Validation Loss:34.7835
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Epoch[17/50], Train Loss:41.6721, Validation Loss:34.2062
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Epoch[18/50], Train Loss:40.7591, Validation Loss:33.2033
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Epoch[19/50], Train Loss:39.7313, Validation Loss:33.5503
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Epoch[20/50], Train Loss:38.9936, Validation Loss:33.1386
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Epoch[21/50], Train Loss:39.2574, Validation Loss:32.4477
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.94it/s]
Epoch[22/50], Train Loss:38.2784, Validation Loss:33.8612
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.85it/s]
Epoch[23/50], Train Loss:38.4653, Validation Loss:31.5379
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.45it/s]
Epoch[24/50], Train Loss:37.6626, Validation Loss:31.3537
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.09it/s]
Epoch[25/50], Train Loss:37.2988, Validation Loss:31.2123
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.32it/s]
Epoch[26/50], Train Loss:37.0096, Validation Loss:31.4618
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:34<00:00, 13.42it/s]
Epoch[27/50], Train Loss:36.8512, Validation Loss:32.7646
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:51<00:00, 8.96it/s]
Epoch[28/50], Train Loss:36.6293, Validation Loss:30.2892
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:53<00:00, 8.55it/s]
Epoch[29/50], Train Loss:37.2254, Validation Loss:31.5364
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:52<00:00, 8.77it/s]
Epoch[30/50], Train Loss:35.7450, Validation Loss:32.0896
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:50<00:00, 8.99it/s]
Epoch[31/50], Train Loss:36.0788, Validation Loss:29.2393
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:50<00:00, 9.09it/s]
Epoch[32/50], Train Loss:36.1950, Validation Loss:30.1364
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:53<00:00, 8.62it/s]
Epoch[33/50], Train Loss:35.3612, Validation Loss:31.5229
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:52<00:00, 8.72it/s]
Epoch[34/50], Train Loss:34.8340, Validation Loss:30.3049
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:53<00:00, 8.60it/s]
Epoch[35/50], Train Loss:34.8637, Validation Loss:30.8694 Early stopping at epoch 35. Running Exp 2: ratio=0.25, bias=32 Exp 2: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:51<00:00, 8.85it/s]
Epoch[1/50], Train Loss:242.5009, Validation Loss:109.2725
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:50<00:00, 9.01it/s]
Epoch[2/50], Train Loss:106.0935, Validation Loss:59.2624
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:53<00:00, 8.58it/s]
Epoch[3/50], Train Loss:80.5898, Validation Loss:50.9127
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:35<00:00, 12.80it/s]
Epoch[4/50], Train Loss:68.6722, Validation Loss:47.9894
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.24it/s]
Epoch[5/50], Train Loss:61.9362, Validation Loss:43.8586
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.72it/s]
Epoch[6/50], Train Loss:58.0273, Validation Loss:40.9959
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.15it/s]
Epoch[7/50], Train Loss:54.8473, Validation Loss:39.1939
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Epoch[8/50], Train Loss:53.2099, Validation Loss:38.2051
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Epoch[9/50], Train Loss:50.6736, Validation Loss:37.6587
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Epoch[10/50], Train Loss:49.4160, Validation Loss:38.6142
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Epoch[11/50], Train Loss:47.4664, Validation Loss:34.8834
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Epoch[12/50], Train Loss:46.8097, Validation Loss:35.2517
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Epoch[13/50], Train Loss:46.2103, Validation Loss:35.4123
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Epoch[14/50], Train Loss:45.0377, Validation Loss:33.0319
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Epoch[15/50], Train Loss:44.7456, Validation Loss:33.0847
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Epoch[16/50], Train Loss:43.4075, Validation Loss:33.6099
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Epoch[17/50], Train Loss:42.9035, Validation Loss:34.0960
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Epoch[18/50], Train Loss:42.6230, Validation Loss:32.9428
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Epoch[19/50], Train Loss:41.6059, Validation Loss:35.7841
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Epoch[20/50], Train Loss:41.0088, Validation Loss:33.8904
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.26it/s]
Epoch[21/50], Train Loss:41.0805, Validation Loss:31.7560
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.86it/s]
Epoch[22/50], Train Loss:40.4790, Validation Loss:32.7508
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.20it/s]
Epoch[23/50], Train Loss:39.6999, Validation Loss:31.9235 Early stopping at epoch 23. Running Exp 3: ratio=0.25, bias=64 Exp 3: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.74it/s]
Epoch[1/50], Train Loss:234.3247, Validation Loss:106.9449
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.95it/s]
Epoch[2/50], Train Loss:113.3797, Validation Loss:65.8595
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.08it/s]
Epoch[3/50], Train Loss:85.0798, Validation Loss:53.1249
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.14it/s]
Epoch[4/50], Train Loss:73.5279, Validation Loss:47.1632
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.85it/s]
Epoch[5/50], Train Loss:66.0507, Validation Loss:42.1871
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.74it/s]
Epoch[6/50], Train Loss:62.0297, Validation Loss:40.7901
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.51it/s]
Epoch[7/50], Train Loss:58.0316, Validation Loss:39.5896
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.44it/s]
Epoch[8/50], Train Loss:56.0046, Validation Loss:40.1821
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.71it/s]
Epoch[9/50], Train Loss:54.1673, Validation Loss:36.1576
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.96it/s]
Epoch[10/50], Train Loss:52.0094, Validation Loss:37.0756
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.71it/s]
Epoch[11/50], Train Loss:50.5939, Validation Loss:35.4244
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 13.94it/s]
Epoch[12/50], Train Loss:49.6295, Validation Loss:34.9026
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.36it/s]
Epoch[13/50], Train Loss:48.5181, Validation Loss:35.0982
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.82it/s]
Epoch[14/50], Train Loss:47.3371, Validation Loss:34.8177
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.52it/s]
Epoch[15/50], Train Loss:46.8723, Validation Loss:34.0612
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.59it/s]
Epoch[16/50], Train Loss:46.0655, Validation Loss:35.0872
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.54it/s]
Epoch[17/50], Train Loss:44.6679, Validation Loss:33.1265
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.39it/s]
Epoch[18/50], Train Loss:44.5013, Validation Loss:33.6769
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.53it/s]
Epoch[19/50], Train Loss:44.0645, Validation Loss:33.5485
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.45it/s]
Epoch[20/50], Train Loss:43.6139, Validation Loss:32.0192
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.52it/s]
Epoch[21/50], Train Loss:42.8631, Validation Loss:32.4956
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.24it/s]
Epoch[22/50], Train Loss:42.4603, Validation Loss:34.5662
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:33<00:00, 13.87it/s]
Epoch[23/50], Train Loss:42.1648, Validation Loss:31.0537
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.32it/s]
Epoch[24/50], Train Loss:40.8681, Validation Loss:31.4674
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.38it/s]
Epoch[25/50], Train Loss:40.8309, Validation Loss:31.1238
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.30it/s]
Epoch[26/50], Train Loss:41.0195, Validation Loss:31.4216
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.55it/s]
Epoch[27/50], Train Loss:40.5640, Validation Loss:31.8691 Early stopping at epoch 27. Running Exp 4: ratio=0.25, bias=128 Exp 4: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.79it/s]
Epoch[1/50], Train Loss:258.0610, Validation Loss:127.7903
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.78it/s]
Epoch[2/50], Train Loss:133.1870, Validation Loss:69.5180
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.62it/s]
Epoch[3/50], Train Loss:97.0935, Validation Loss:54.4414
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.63it/s]
Epoch[4/50], Train Loss:82.6184, Validation Loss:50.2058
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.67it/s]
Epoch[5/50], Train Loss:73.2662, Validation Loss:46.5111
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.16it/s]
Epoch[6/50], Train Loss:67.8280, Validation Loss:43.3420
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.67it/s]
Epoch[7/50], Train Loss:63.8529, Validation Loss:42.2836
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.25it/s]
Epoch[8/50], Train Loss:61.2187, Validation Loss:38.8033
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.23it/s]
Epoch[9/50], Train Loss:58.7565, Validation Loss:38.5588
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.02it/s]
Epoch[10/50], Train Loss:57.4511, Validation Loss:38.9159
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.77it/s]
Epoch[11/50], Train Loss:55.7196, Validation Loss:37.3772
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.13it/s]
Epoch[12/50], Train Loss:54.8164, Validation Loss:36.8795
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.91it/s]
Epoch[13/50], Train Loss:53.5573, Validation Loss:35.0091
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.90it/s]
Epoch[14/50], Train Loss:52.2718, Validation Loss:35.6722
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.75it/s]
Epoch[15/50], Train Loss:51.0319, Validation Loss:33.9644
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.83it/s]
Epoch[16/50], Train Loss:50.5708, Validation Loss:34.6281
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.74it/s]
Epoch[17/50], Train Loss:49.6870, Validation Loss:34.0964
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.53it/s]
Epoch[18/50], Train Loss:49.4047, Validation Loss:33.9392
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.80it/s]
Epoch[19/50], Train Loss:48.0313, Validation Loss:32.5398
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.12it/s]
Epoch[20/50], Train Loss:47.0080, Validation Loss:34.2102
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.93it/s]
Epoch[21/50], Train Loss:47.2905, Validation Loss:32.9627
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.52it/s]
Epoch[22/50], Train Loss:47.0839, Validation Loss:33.1695
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.23it/s]
Epoch[23/50], Train Loss:46.0823, Validation Loss:32.3446
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.63it/s]
Epoch[24/50], Train Loss:45.6884, Validation Loss:32.2287
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.10it/s]
Epoch[25/50], Train Loss:45.2804, Validation Loss:32.0515
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.51it/s]
Epoch[26/50], Train Loss:44.9240, Validation Loss:31.7220
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.66it/s]
Epoch[27/50], Train Loss:43.8375, Validation Loss:30.9849
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.56it/s]
Epoch[28/50], Train Loss:43.5789, Validation Loss:31.7975
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.76it/s]
Epoch[29/50], Train Loss:43.2911, Validation Loss:31.0786
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.72it/s]
Epoch[30/50], Train Loss:43.1325, Validation Loss:31.0011
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.69it/s]
Epoch[31/50], Train Loss:43.0112, Validation Loss:31.3176
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.21it/s]
Epoch[32/50], Train Loss:43.2696, Validation Loss:30.9693
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.33it/s]
Epoch[33/50], Train Loss:42.6706, Validation Loss:31.7327
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.29it/s]
Epoch[34/50], Train Loss:41.3953, Validation Loss:32.1171
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.24it/s]
Epoch[35/50], Train Loss:41.6246, Validation Loss:30.9105
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.30it/s]
Epoch[36/50], Train Loss:41.8942, Validation Loss:31.7112
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.35it/s]
Epoch[37/50], Train Loss:40.9547, Validation Loss:29.7683
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.79it/s]
Epoch[38/50], Train Loss:40.6319, Validation Loss:29.2994
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.14it/s]
Epoch[39/50], Train Loss:40.8729, Validation Loss:29.8186
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.35it/s]
Epoch[40/50], Train Loss:40.5147, Validation Loss:30.0766
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.31it/s]
Epoch[41/50], Train Loss:40.8524, Validation Loss:29.7907 Early stopping at epoch 41. Running Exp 5: ratio=0.42, bias=0 Exp 5: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.85it/s]
Epoch[1/50], Train Loss:239.3419, Validation Loss:116.4504
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.29it/s]
Epoch[2/50], Train Loss:110.9938, Validation Loss:68.7165
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.46it/s]
Epoch[3/50], Train Loss:83.6223, Validation Loss:59.4375
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.42it/s]
Epoch[4/50], Train Loss:70.7624, Validation Loss:51.5616
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.15it/s]
Epoch[5/50], Train Loss:64.1925, Validation Loss:49.0023
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.84it/s]
Epoch[6/50], Train Loss:60.0185, Validation Loss:43.1175
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.67it/s]
Epoch[7/50], Train Loss:56.2287, Validation Loss:44.5035
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.65it/s]
Epoch[8/50], Train Loss:54.0676, Validation Loss:42.2196
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.01it/s]
Epoch[9/50], Train Loss:51.3506, Validation Loss:41.2693
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.11it/s]
Epoch[10/50], Train Loss:49.9700, Validation Loss:39.2000
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.39it/s]
Epoch[11/50], Train Loss:48.5401, Validation Loss:39.7281
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.87it/s]
Epoch[12/50], Train Loss:47.4773, Validation Loss:36.9053
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.89it/s]
Epoch[13/50], Train Loss:45.6932, Validation Loss:37.7450
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.01it/s]
Epoch[14/50], Train Loss:45.0534, Validation Loss:36.4257
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.50it/s]
Epoch[15/50], Train Loss:44.4532, Validation Loss:36.7379
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.87it/s]
Epoch[16/50], Train Loss:43.7282, Validation Loss:37.0801
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.45it/s]
Epoch[17/50], Train Loss:42.7301, Validation Loss:35.0245
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.86it/s]
Epoch[18/50], Train Loss:42.4773, Validation Loss:36.4931
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.39it/s]
Epoch[19/50], Train Loss:41.5731, Validation Loss:34.5837
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.31it/s]
Epoch[20/50], Train Loss:40.8725, Validation Loss:35.8258
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.22it/s]
Epoch[21/50], Train Loss:41.3731, Validation Loss:34.1272
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.83it/s]
Epoch[22/50], Train Loss:40.0899, Validation Loss:34.2547
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.00it/s]
Epoch[23/50], Train Loss:39.5795, Validation Loss:35.1708
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.78it/s]
Epoch[24/50], Train Loss:39.2582, Validation Loss:33.9364
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.01it/s]
Epoch[25/50], Train Loss:38.2028, Validation Loss:33.3322
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.92it/s]
Epoch[26/50], Train Loss:38.4303, Validation Loss:31.7747
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.13it/s]
Epoch[27/50], Train Loss:38.5113, Validation Loss:33.6634
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.43it/s]
Epoch[28/50], Train Loss:38.2486, Validation Loss:33.8067
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.47it/s]
Epoch[29/50], Train Loss:37.9116, Validation Loss:31.7645
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.30it/s]
Epoch[30/50], Train Loss:37.4615, Validation Loss:33.3641
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.15it/s]
Epoch[31/50], Train Loss:36.8215, Validation Loss:33.2336
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.22it/s]
Epoch[32/50], Train Loss:36.9946, Validation Loss:31.4417
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.46it/s]
Epoch[33/50], Train Loss:36.4334, Validation Loss:32.9326
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.59it/s]
Epoch[34/50], Train Loss:36.7405, Validation Loss:32.7099
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.00it/s]
Epoch[35/50], Train Loss:36.2103, Validation Loss:31.2330
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.34it/s]
Epoch[36/50], Train Loss:35.9576, Validation Loss:31.9494
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.04it/s]
Epoch[37/50], Train Loss:35.2325, Validation Loss:32.3760
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.80it/s]
Epoch[38/50], Train Loss:35.9474, Validation Loss:31.9616 Early stopping at epoch 38. Running Exp 6: ratio=0.42, bias=32 Exp 6: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.51it/s]
Epoch[1/50], Train Loss:253.5781, Validation Loss:140.6226
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.73it/s]
Epoch[2/50], Train Loss:124.4685, Validation Loss:71.0167
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.86it/s]
Epoch[3/50], Train Loss:88.2669, Validation Loss:56.6351
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.85it/s]
Epoch[4/50], Train Loss:75.8125, Validation Loss:53.6357
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.64it/s]
Epoch[5/50], Train Loss:68.0014, Validation Loss:49.6689
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.21it/s]
Epoch[6/50], Train Loss:62.7313, Validation Loss:47.1199
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.35it/s]
Epoch[7/50], Train Loss:59.2277, Validation Loss:45.4146
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.97it/s]
Epoch[8/50], Train Loss:57.3990, Validation Loss:42.0453
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.13it/s]
Epoch[9/50], Train Loss:54.3394, Validation Loss:41.3249
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.20it/s]
Epoch[10/50], Train Loss:52.2777, Validation Loss:40.1539
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.51it/s]
Epoch[11/50], Train Loss:51.6382, Validation Loss:39.1770
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.39it/s]
Epoch[12/50], Train Loss:50.7190, Validation Loss:38.5450
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.84it/s]
Epoch[13/50], Train Loss:48.9377, Validation Loss:38.1539
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.51it/s]
Epoch[14/50], Train Loss:47.5612, Validation Loss:37.6670
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.64it/s]
Epoch[15/50], Train Loss:46.7647, Validation Loss:35.9434
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.27it/s]
Epoch[16/50], Train Loss:45.8556, Validation Loss:35.7843
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.32it/s]
Epoch[17/50], Train Loss:45.2970, Validation Loss:36.0588
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.14it/s]
Epoch[18/50], Train Loss:44.3147, Validation Loss:36.5364
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.37it/s]
Epoch[19/50], Train Loss:44.2777, Validation Loss:36.2827
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.07it/s]
Epoch[20/50], Train Loss:43.4538, Validation Loss:35.4436
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.42it/s]
Epoch[21/50], Train Loss:42.8564, Validation Loss:35.0690
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.61it/s]
Epoch[22/50], Train Loss:42.2249, Validation Loss:35.5498
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.07it/s]
Epoch[23/50], Train Loss:42.5955, Validation Loss:37.6225
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.62it/s]
Epoch[24/50], Train Loss:41.9176, Validation Loss:34.7871
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.17it/s]
Epoch[25/50], Train Loss:41.5128, Validation Loss:33.1377
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.04it/s]
Epoch[26/50], Train Loss:41.5142, Validation Loss:33.8698
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.71it/s]
Epoch[27/50], Train Loss:41.0804, Validation Loss:35.3986
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.29it/s]
Epoch[28/50], Train Loss:40.1465, Validation Loss:34.6361
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.62it/s]
Epoch[29/50], Train Loss:40.1528, Validation Loss:32.6834
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.73it/s]
Epoch[30/50], Train Loss:39.5131, Validation Loss:34.4398
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.86it/s]
Epoch[31/50], Train Loss:39.8206, Validation Loss:33.1898
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.29it/s]
Epoch[32/50], Train Loss:39.2507, Validation Loss:34.4827 Early stopping at epoch 32. Running Exp 7: ratio=0.42, bias=64 Exp 7: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.37it/s]
Epoch[1/50], Train Loss:248.9161, Validation Loss:119.7257
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:33<00:00, 13.80it/s]
Epoch[2/50], Train Loss:117.4154, Validation Loss:68.7506
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.31it/s]
Epoch[3/50], Train Loss:85.9293, Validation Loss:54.5103
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.39it/s]
Epoch[4/50], Train Loss:73.0295, Validation Loss:49.0951
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.42it/s]
Epoch[5/50], Train Loss:66.5991, Validation Loss:43.4212
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.41it/s]
Epoch[6/50], Train Loss:61.9568, Validation Loss:43.5533
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.08it/s]
Epoch[7/50], Train Loss:58.8355, Validation Loss:42.7938
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.20it/s]
Epoch[8/50], Train Loss:56.3391, Validation Loss:40.5382
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.21it/s]
Epoch[9/50], Train Loss:53.8806, Validation Loss:37.8963
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 13.97it/s]
Epoch[10/50], Train Loss:52.2276, Validation Loss:37.2299
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.32it/s]
Epoch[11/50], Train Loss:51.2786, Validation Loss:37.3651
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 13.94it/s]
Epoch[12/50], Train Loss:50.3158, Validation Loss:37.4728
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.30it/s]
Epoch[13/50], Train Loss:48.7333, Validation Loss:35.3341
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.07it/s]
Epoch[14/50], Train Loss:48.3969, Validation Loss:38.5591
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.51it/s]
Epoch[15/50], Train Loss:47.2308, Validation Loss:34.6876
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.47it/s]
Epoch[16/50], Train Loss:45.5696, Validation Loss:35.0653
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.75it/s]
Epoch[17/50], Train Loss:45.2760, Validation Loss:33.5780
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.64it/s]
Epoch[18/50], Train Loss:44.7070, Validation Loss:35.5935
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.13it/s]
Epoch[19/50], Train Loss:44.4894, Validation Loss:34.9586
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.22it/s]
Epoch[20/50], Train Loss:43.3408, Validation Loss:34.7399
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.15it/s]
Epoch[21/50], Train Loss:43.8358, Validation Loss:33.3091
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.46it/s]
Epoch[22/50], Train Loss:43.1123, Validation Loss:32.2388
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.73it/s]
Epoch[23/50], Train Loss:43.0078, Validation Loss:32.2672
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.54it/s]
Epoch[24/50], Train Loss:42.0795, Validation Loss:32.0709
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.28it/s]
Epoch[25/50], Train Loss:41.8912, Validation Loss:30.8461
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.04it/s]
Epoch[26/50], Train Loss:41.5975, Validation Loss:33.6436
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.94it/s]
Epoch[27/50], Train Loss:40.8309, Validation Loss:33.1750
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.62it/s]
Epoch[28/50], Train Loss:40.9638, Validation Loss:33.1142
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.08it/s]
Epoch[29/50], Train Loss:40.4856, Validation Loss:31.6366
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.30it/s]
Epoch[30/50], Train Loss:39.7259, Validation Loss:30.4557
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.21it/s]
Epoch[31/50], Train Loss:39.9639, Validation Loss:31.4548
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.82it/s]
Epoch[32/50], Train Loss:39.6990, Validation Loss:32.9844 Early stopping at epoch 32. Running Exp 8: ratio=0.42, bias=128 Exp 8: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.65it/s]
Epoch[1/50], Train Loss:247.4205, Validation Loss:126.7729
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.02it/s]
Epoch[2/50], Train Loss:128.3023, Validation Loss:77.6286
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.45it/s]
Epoch[3/50], Train Loss:99.4747, Validation Loss:64.0411
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.37it/s]
Epoch[4/50], Train Loss:86.5183, Validation Loss:54.6844
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.33it/s]
Epoch[5/50], Train Loss:77.2906, Validation Loss:51.7172
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.98it/s]
Epoch[6/50], Train Loss:71.6655, Validation Loss:46.4837
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.71it/s]
Epoch[7/50], Train Loss:68.5589, Validation Loss:45.1748
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.97it/s]
Epoch[8/50], Train Loss:64.6710, Validation Loss:46.1428
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.21it/s]
Epoch[9/50], Train Loss:61.8859, Validation Loss:41.0623
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.72it/s]
Epoch[10/50], Train Loss:59.7280, Validation Loss:42.4264
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.92it/s]
Epoch[11/50], Train Loss:58.7628, Validation Loss:41.2259
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 13.88it/s]
Epoch[12/50], Train Loss:56.2597, Validation Loss:39.8954
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.04it/s]
Epoch[13/50], Train Loss:55.9695, Validation Loss:44.1905
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 14.26it/s]
Epoch[14/50], Train Loss:55.5910, Validation Loss:40.8076
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.36it/s]
Epoch[15/50], Train Loss:52.6900, Validation Loss:38.6659
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:32<00:00, 13.88it/s]
Epoch[16/50], Train Loss:52.7320, Validation Loss:39.3040
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.01it/s]
Epoch[17/50], Train Loss:51.8509, Validation Loss:38.5398
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.33it/s]
Epoch[18/50], Train Loss:51.3966, Validation Loss:39.3482
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.08it/s]
Epoch[19/50], Train Loss:50.6231, Validation Loss:37.2660
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.49it/s]
Epoch[20/50], Train Loss:50.1914, Validation Loss:37.5376
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.05it/s]
Epoch[21/50], Train Loss:48.9343, Validation Loss:37.3000
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.43it/s]
Epoch[22/50], Train Loss:48.2468, Validation Loss:37.2184
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.02it/s]
Epoch[23/50], Train Loss:47.8887, Validation Loss:36.3210
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.38it/s]
Epoch[24/50], Train Loss:47.1059, Validation Loss:35.6126
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.67it/s]
Epoch[25/50], Train Loss:46.7907, Validation Loss:36.9742
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.40it/s]
Epoch[26/50], Train Loss:46.4225, Validation Loss:35.0174
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.64it/s]
Epoch[27/50], Train Loss:46.5521, Validation Loss:35.2392
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.34it/s]
Epoch[28/50], Train Loss:46.2046, Validation Loss:35.9143
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.95it/s]
Epoch[29/50], Train Loss:45.7744, Validation Loss:34.8507
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.96it/s]
Epoch[30/50], Train Loss:45.0896, Validation Loss:34.6494
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.25it/s]
Epoch[31/50], Train Loss:44.8822, Validation Loss:35.7703
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.95it/s]
Epoch[32/50], Train Loss:44.4890, Validation Loss:35.0169
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.28it/s]
Epoch[33/50], Train Loss:44.2183, Validation Loss:34.6802
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.53it/s]
Epoch[34/50], Train Loss:44.1526, Validation Loss:33.8435
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.78it/s]
Epoch[35/50], Train Loss:44.1694, Validation Loss:33.3475
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:31<00:00, 14.69it/s]
Epoch[36/50], Train Loss:43.4067, Validation Loss:34.1924
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 14.84it/s]
Epoch[37/50], Train Loss:43.4800, Validation Loss:32.8098
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.33it/s]
Epoch[38/50], Train Loss:42.5909, Validation Loss:33.3639
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:33<00:00, 13.62it/s]
Epoch[39/50], Train Loss:42.7284, Validation Loss:33.4526
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:30<00:00, 15.10it/s]
Epoch[40/50], Train Loss:42.1800, Validation Loss:31.9997
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.21it/s]
Epoch[41/50], Train Loss:42.4264, Validation Loss:33.2761
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.96it/s]
Epoch[42/50], Train Loss:42.3194, Validation Loss:32.2204
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.89it/s]
Epoch[43/50], Train Loss:41.5299, Validation Loss:34.1281 Early stopping at epoch 43. Running Exp 9: ratio=0.58, bias=0 Exp 9: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.91it/s]
Epoch[1/50], Train Loss:230.6305, Validation Loss:98.2488
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.45it/s]
Epoch[2/50], Train Loss:101.6813, Validation Loss:65.5486
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.81it/s]
Epoch[3/50], Train Loss:76.3630, Validation Loss:53.6747
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.91it/s]
Epoch[4/50], Train Loss:66.9350, Validation Loss:48.0931
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.76it/s]
Epoch[5/50], Train Loss:60.1857, Validation Loss:45.6974
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.22it/s]
Epoch[6/50], Train Loss:57.1056, Validation Loss:42.9977
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.44it/s]
Epoch[7/50], Train Loss:53.7894, Validation Loss:41.7265
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.18it/s]
Epoch[8/50], Train Loss:51.4102, Validation Loss:41.2509
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.67it/s]
Epoch[9/50], Train Loss:49.5138, Validation Loss:38.7848
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.91it/s]
Epoch[10/50], Train Loss:47.7154, Validation Loss:38.7656
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.18it/s]
Epoch[11/50], Train Loss:46.7974, Validation Loss:39.0771
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.28it/s]
Epoch[12/50], Train Loss:46.1249, Validation Loss:36.3580
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.85it/s]
Epoch[13/50], Train Loss:44.4941, Validation Loss:36.7343
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.46it/s]
Epoch[14/50], Train Loss:43.3832, Validation Loss:37.3049
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.58it/s]
Epoch[15/50], Train Loss:43.6337, Validation Loss:35.4724
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.74it/s]
Epoch[16/50], Train Loss:42.8467, Validation Loss:37.1660
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.12it/s]
Epoch[17/50], Train Loss:41.8568, Validation Loss:35.1276
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.15it/s]
Epoch[18/50], Train Loss:41.2300, Validation Loss:34.5677
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.15it/s]
Epoch[19/50], Train Loss:40.6876, Validation Loss:34.8984
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.03it/s]
Epoch[20/50], Train Loss:40.3703, Validation Loss:34.0998
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.07it/s]
Epoch[21/50], Train Loss:39.9892, Validation Loss:35.9339
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.43it/s]
Epoch[22/50], Train Loss:39.8215, Validation Loss:34.0771
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.17it/s]
Epoch[23/50], Train Loss:38.5440, Validation Loss:33.5713
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.82it/s]
Epoch[24/50], Train Loss:38.6241, Validation Loss:35.0727
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.31it/s]
Epoch[25/50], Train Loss:38.2466, Validation Loss:33.2783
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.48it/s]
Epoch[26/50], Train Loss:38.2134, Validation Loss:32.6124
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.10it/s]
Epoch[27/50], Train Loss:37.2414, Validation Loss:32.8869
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.08it/s]
Epoch[28/50], Train Loss:36.8968, Validation Loss:32.6287
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.65it/s]
Epoch[29/50], Train Loss:37.3912, Validation Loss:33.2337
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.10it/s]
Epoch[30/50], Train Loss:36.9214, Validation Loss:32.8941
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.62it/s]
Epoch[31/50], Train Loss:35.9187, Validation Loss:33.3685
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.51it/s]
Epoch[32/50], Train Loss:36.3190, Validation Loss:32.7111 Early stopping at epoch 32. Running Exp 10: ratio=0.58, bias=32 Exp 10: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.81it/s]
Epoch[1/50], Train Loss:263.8376, Validation Loss:143.9485
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.13it/s]
Epoch[2/50], Train Loss:129.3626, Validation Loss:70.9920
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.87it/s]
Epoch[3/50], Train Loss:85.0982, Validation Loss:54.0873
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.71it/s]
Epoch[4/50], Train Loss:71.6407, Validation Loss:49.3965
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.08it/s]
Epoch[5/50], Train Loss:65.4529, Validation Loss:45.3685
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.69it/s]
Epoch[6/50], Train Loss:60.5541, Validation Loss:43.9423
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.47it/s]
Epoch[7/50], Train Loss:57.6973, Validation Loss:42.5696
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.46it/s]
Epoch[8/50], Train Loss:55.0125, Validation Loss:41.4011
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.61it/s]
Epoch[9/50], Train Loss:53.2929, Validation Loss:40.9081
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.57it/s]
Epoch[10/50], Train Loss:51.5456, Validation Loss:38.9086
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.54it/s]
Epoch[11/50], Train Loss:50.1241, Validation Loss:37.2236
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.20it/s]
Epoch[12/50], Train Loss:48.9079, Validation Loss:37.6812
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.29it/s]
Epoch[13/50], Train Loss:48.0946, Validation Loss:36.2518
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.84it/s]
Epoch[14/50], Train Loss:47.4750, Validation Loss:37.2647
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.91it/s]
Epoch[15/50], Train Loss:46.2884, Validation Loss:35.8828
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.99it/s]
Epoch[16/50], Train Loss:46.2639, Validation Loss:35.2547
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.16it/s]
Epoch[17/50], Train Loss:44.8653, Validation Loss:35.6622
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.22it/s]
Epoch[18/50], Train Loss:44.1860, Validation Loss:34.2919
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.32it/s]
Epoch[19/50], Train Loss:43.4950, Validation Loss:34.5428
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.61it/s]
Epoch[20/50], Train Loss:43.4374, Validation Loss:34.3848
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.09it/s]
Epoch[21/50], Train Loss:42.9572, Validation Loss:33.4879
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.03it/s]
Epoch[22/50], Train Loss:42.4983, Validation Loss:35.8355
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.16it/s]
Epoch[23/50], Train Loss:41.4410, Validation Loss:33.1430
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.24it/s]
Epoch[24/50], Train Loss:41.3788, Validation Loss:33.0447
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.93it/s]
Epoch[25/50], Train Loss:41.3893, Validation Loss:34.0910
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.83it/s]
Epoch[26/50], Train Loss:40.6261, Validation Loss:32.3378
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.00it/s]
Epoch[27/50], Train Loss:40.3155, Validation Loss:32.6447
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.69it/s]
Epoch[28/50], Train Loss:39.8995, Validation Loss:32.4222
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.94it/s]
Epoch[29/50], Train Loss:39.3906, Validation Loss:33.9256
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.73it/s]
Epoch[30/50], Train Loss:39.1317, Validation Loss:34.1374
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.08it/s]
Epoch[31/50], Train Loss:38.2857, Validation Loss:33.0926
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.12it/s]
Epoch[32/50], Train Loss:38.5960, Validation Loss:33.8875 Early stopping at epoch 32. Running Exp 11: ratio=0.58, bias=64 Exp 11: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.57it/s]
Epoch[1/50], Train Loss:261.4572, Validation Loss:137.7467
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:29<00:00, 15.75it/s]
Epoch[2/50], Train Loss:128.1288, Validation Loss:73.4249
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.17it/s]
Epoch[3/50], Train Loss:92.8358, Validation Loss:60.4377
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.11it/s]
Epoch[4/50], Train Loss:78.9211, Validation Loss:54.4958
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.67it/s]
Epoch[5/50], Train Loss:71.0349, Validation Loss:50.2038
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.29it/s]
Epoch[6/50], Train Loss:65.4414, Validation Loss:46.6534
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.82it/s]
Epoch[7/50], Train Loss:62.0034, Validation Loss:45.6173
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.92it/s]
Epoch[8/50], Train Loss:59.2700, Validation Loss:42.2975
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.55it/s]
Epoch[9/50], Train Loss:56.8139, Validation Loss:43.9484
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.43it/s]
Epoch[10/50], Train Loss:54.1729, Validation Loss:40.8465
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.51it/s]
Epoch[11/50], Train Loss:53.2639, Validation Loss:40.3728
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.09it/s]
Epoch[12/50], Train Loss:52.3229, Validation Loss:40.1815
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.28it/s]
Epoch[13/50], Train Loss:50.3557, Validation Loss:38.2729
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.32it/s]
Epoch[14/50], Train Loss:49.4192, Validation Loss:38.1540
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.08it/s]
Epoch[15/50], Train Loss:49.0008, Validation Loss:38.0491
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.58it/s]
Epoch[16/50], Train Loss:48.1010, Validation Loss:37.0477
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.30it/s]
Epoch[17/50], Train Loss:47.3862, Validation Loss:37.9391
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 16.99it/s]
Epoch[18/50], Train Loss:46.6179, Validation Loss:39.0879
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.60it/s]
Epoch[19/50], Train Loss:46.3291, Validation Loss:36.8723
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.11it/s]
Epoch[20/50], Train Loss:45.4012, Validation Loss:37.1694
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.28it/s]
Epoch[21/50], Train Loss:45.0881, Validation Loss:35.9300
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.48it/s]
Epoch[22/50], Train Loss:44.0668, Validation Loss:34.4929
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.67it/s]
Epoch[23/50], Train Loss:44.2046, Validation Loss:36.2088
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.48it/s]
Epoch[24/50], Train Loss:43.1456, Validation Loss:35.8325
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.58it/s]
Epoch[25/50], Train Loss:43.4936, Validation Loss:34.2269
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.80it/s]
Epoch[26/50], Train Loss:42.1657, Validation Loss:34.7712
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.21it/s]
Epoch[27/50], Train Loss:41.7566, Validation Loss:34.9109
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.23it/s]
Epoch[28/50], Train Loss:41.7304, Validation Loss:35.2736
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.30it/s]
Epoch[29/50], Train Loss:41.4529, Validation Loss:34.5772
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.40it/s]
Epoch[30/50], Train Loss:41.0294, Validation Loss:32.7179
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.43it/s]
Epoch[31/50], Train Loss:41.3097, Validation Loss:33.8658
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.08it/s]
Epoch[32/50], Train Loss:41.0216, Validation Loss:33.8137 Early stopping at epoch 32. Running Exp 12: ratio=0.58, bias=128 Exp 12: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.25it/s]
Epoch[1/50], Train Loss:273.0484, Validation Loss:165.3034
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.64it/s]
Epoch[2/50], Train Loss:142.6462, Validation Loss:77.2411
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.18it/s]
Epoch[3/50], Train Loss:102.8818, Validation Loss:60.7668
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.91it/s]
Epoch[4/50], Train Loss:86.5342, Validation Loss:53.8590
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.15it/s]
Epoch[5/50], Train Loss:76.7713, Validation Loss:49.4640
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.63it/s]
Epoch[6/50], Train Loss:71.2253, Validation Loss:49.5381
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.30it/s]
Epoch[7/50], Train Loss:67.8310, Validation Loss:44.7510
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.60it/s]
Epoch[8/50], Train Loss:64.2407, Validation Loss:47.0967
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.39it/s]
Epoch[9/50], Train Loss:61.9664, Validation Loss:42.4922
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.45it/s]
Epoch[10/50], Train Loss:59.7601, Validation Loss:41.9611
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.49it/s]
Epoch[11/50], Train Loss:57.5876, Validation Loss:42.9497
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.64it/s]
Epoch[12/50], Train Loss:56.5547, Validation Loss:39.1433
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.72it/s]
Epoch[13/50], Train Loss:55.4405, Validation Loss:40.2251
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.48it/s]
Epoch[14/50], Train Loss:53.8106, Validation Loss:37.6768
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.56it/s]
Epoch[15/50], Train Loss:53.3739, Validation Loss:40.1162
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.62it/s]
Epoch[16/50], Train Loss:51.8962, Validation Loss:36.8200
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.34it/s]
Epoch[17/50], Train Loss:51.1779, Validation Loss:37.4535
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.56it/s]
Epoch[18/50], Train Loss:50.6345, Validation Loss:35.2185
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.63it/s]
Epoch[19/50], Train Loss:49.7525, Validation Loss:35.6148
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.81it/s]
Epoch[20/50], Train Loss:49.2387, Validation Loss:37.4426
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.90it/s]
Epoch[21/50], Train Loss:48.8461, Validation Loss:37.2290
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 18.24it/s]
Epoch[22/50], Train Loss:48.6374, Validation Loss:35.2438
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.54it/s]
Epoch[23/50], Train Loss:47.1981, Validation Loss:35.3113
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.98it/s]
Epoch[24/50], Train Loss:47.4002, Validation Loss:33.5624
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.98it/s]
Epoch[25/50], Train Loss:46.4532, Validation Loss:34.9594
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.61it/s]
Epoch[26/50], Train Loss:47.1290, Validation Loss:33.9471 Early stopping at epoch 26. Running Exp 13: ratio=0.75, bias=0 Exp 13: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.52it/s]
Epoch[1/50], Train Loss:200.8284, Validation Loss:81.0237
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.31it/s]
Epoch[2/50], Train Loss:85.9262, Validation Loss:55.2329
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.59it/s]
Epoch[3/50], Train Loss:66.4079, Validation Loss:48.1654
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.41it/s]
Epoch[4/50], Train Loss:58.2486, Validation Loss:45.2400
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.47it/s]
Epoch[5/50], Train Loss:52.5912, Validation Loss:45.8423
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.46it/s]
Epoch[6/50], Train Loss:50.0977, Validation Loss:41.2900
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.39it/s]
Epoch[7/50], Train Loss:46.3597, Validation Loss:37.7294
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.50it/s]
Epoch[8/50], Train Loss:44.8779, Validation Loss:37.3179
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.28it/s]
Epoch[9/50], Train Loss:43.5092, Validation Loss:37.2591
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 18.14it/s]
Epoch[10/50], Train Loss:41.7478, Validation Loss:35.4928
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.30it/s]
Epoch[11/50], Train Loss:40.6193, Validation Loss:34.9409
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.49it/s]
Epoch[12/50], Train Loss:39.5259, Validation Loss:33.8732
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.55it/s]
Epoch[13/50], Train Loss:38.5724, Validation Loss:34.2898
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.33it/s]
Epoch[14/50], Train Loss:38.2470, Validation Loss:33.5359
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.67it/s]
Epoch[15/50], Train Loss:37.4956, Validation Loss:33.3075
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.57it/s]
Epoch[16/50], Train Loss:36.3580, Validation Loss:32.1132
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.47it/s]
Epoch[17/50], Train Loss:35.5730, Validation Loss:33.4195
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.65it/s]
Epoch[18/50], Train Loss:35.2492, Validation Loss:32.8338
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.78it/s]
Epoch[19/50], Train Loss:35.6420, Validation Loss:33.2692
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.66it/s]
Epoch[20/50], Train Loss:34.7718, Validation Loss:32.3209
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 18.29it/s]
Epoch[21/50], Train Loss:34.0735, Validation Loss:30.9047
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:22<00:00, 20.11it/s]
Epoch[22/50], Train Loss:33.5564, Validation Loss:32.0773
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:23<00:00, 19.29it/s]
Epoch[23/50], Train Loss:33.1887, Validation Loss:32.6027
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.55it/s]
Epoch[24/50], Train Loss:33.1082, Validation Loss:33.3116 Early stopping at epoch 24. Running Exp 14: ratio=0.75, bias=32 Exp 14: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.60it/s]
Epoch[1/50], Train Loss:254.6869, Validation Loss:116.1962
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.71it/s]
Epoch[2/50], Train Loss:111.5933, Validation Loss:71.3500
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.63it/s]
Epoch[3/50], Train Loss:80.2314, Validation Loss:60.9171
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.98it/s]
Epoch[4/50], Train Loss:67.8909, Validation Loss:48.6356
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.52it/s]
Epoch[5/50], Train Loss:61.2893, Validation Loss:47.0227
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.66it/s]
Epoch[6/50], Train Loss:56.9324, Validation Loss:42.3156
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.41it/s]
Epoch[7/50], Train Loss:54.0689, Validation Loss:40.4444
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.25it/s]
Epoch[8/50], Train Loss:51.5269, Validation Loss:40.3466
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.30it/s]
Epoch[9/50], Train Loss:48.9943, Validation Loss:38.0113
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.53it/s]
Epoch[10/50], Train Loss:47.6694, Validation Loss:37.3042
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.60it/s]
Epoch[11/50], Train Loss:46.9082, Validation Loss:35.8256
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.66it/s]
Epoch[12/50], Train Loss:45.0367, Validation Loss:39.7035
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.74it/s]
Epoch[13/50], Train Loss:43.6291, Validation Loss:36.6735
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.45it/s]
Epoch[14/50], Train Loss:43.4003, Validation Loss:35.4047
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.83it/s]
Epoch[15/50], Train Loss:42.3973, Validation Loss:36.1015
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.51it/s]
Epoch[16/50], Train Loss:41.7352, Validation Loss:34.6318
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.20it/s]
Epoch[17/50], Train Loss:41.0027, Validation Loss:35.2152
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.85it/s]
Epoch[18/50], Train Loss:40.3261, Validation Loss:33.3519
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.69it/s]
Epoch[19/50], Train Loss:39.5343, Validation Loss:32.9660
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.06it/s]
Epoch[20/50], Train Loss:39.1993, Validation Loss:32.8689
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.79it/s]
Epoch[21/50], Train Loss:38.5846, Validation Loss:34.1029
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.50it/s]
Epoch[22/50], Train Loss:37.7516, Validation Loss:33.7387
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.88it/s]
Epoch[23/50], Train Loss:38.1717, Validation Loss:34.0717
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.41it/s]
Epoch[24/50], Train Loss:37.6593, Validation Loss:33.7110
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.96it/s]
Epoch[25/50], Train Loss:37.0923, Validation Loss:32.9627
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.78it/s]
Epoch[26/50], Train Loss:36.2541, Validation Loss:31.9282
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.01it/s]
Epoch[27/50], Train Loss:35.9428, Validation Loss:32.7713
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.38it/s]
Epoch[28/50], Train Loss:36.3873, Validation Loss:32.3920 Early stopping at epoch 28. Running Exp 15: ratio=0.75, bias=64 Exp 15: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.06it/s]
Epoch[1/50], Train Loss:233.0208, Validation Loss:106.7072
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.02it/s]
Epoch[2/50], Train Loss:109.1379, Validation Loss:68.3491
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 16.97it/s]
Epoch[3/50], Train Loss:84.5143, Validation Loss:60.4757
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.69it/s]
Epoch[4/50], Train Loss:72.4621, Validation Loss:54.5941
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.58it/s]
Epoch[5/50], Train Loss:65.0470, Validation Loss:48.2979
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.16it/s]
Epoch[6/50], Train Loss:60.2123, Validation Loss:43.8355
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.17it/s]
Epoch[7/50], Train Loss:56.3698, Validation Loss:43.8265
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.45it/s]
Epoch[8/50], Train Loss:53.2022, Validation Loss:39.7396
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.96it/s]
Epoch[9/50], Train Loss:51.1794, Validation Loss:40.7389
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.24it/s]
Epoch[10/50], Train Loss:49.3778, Validation Loss:37.9635
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:23<00:00, 19.37it/s]
Epoch[11/50], Train Loss:47.8270, Validation Loss:38.9472
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 18.28it/s]
Epoch[12/50], Train Loss:46.5512, Validation Loss:36.5885
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:25<00:00, 17.78it/s]
Epoch[13/50], Train Loss:45.1725, Validation Loss:36.2864
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:26<00:00, 17.29it/s]
Epoch[14/50], Train Loss:43.9408, Validation Loss:35.7441
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.57it/s]
Epoch[15/50], Train Loss:43.1664, Validation Loss:37.6093
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.21it/s]
Epoch[16/50], Train Loss:42.8777, Validation Loss:35.4484
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.44it/s]
Epoch[17/50], Train Loss:42.1122, Validation Loss:38.3878
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.25it/s]
Epoch[18/50], Train Loss:41.3632, Validation Loss:34.4780
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.14it/s]
Epoch[19/50], Train Loss:40.6536, Validation Loss:33.9655
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.93it/s]
Epoch[20/50], Train Loss:40.0100, Validation Loss:35.1295
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.41it/s]
Epoch[21/50], Train Loss:39.5173, Validation Loss:34.0693
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.37it/s]
Epoch[22/50], Train Loss:38.2392, Validation Loss:32.8912
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.12it/s]
Epoch[23/50], Train Loss:38.5439, Validation Loss:33.4111
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.17it/s]
Epoch[24/50], Train Loss:37.8635, Validation Loss:33.5726
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.33it/s]
Epoch[25/50], Train Loss:37.7477, Validation Loss:33.5341
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.58it/s]
Epoch[26/50], Train Loss:37.0430, Validation Loss:33.8595
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.78it/s]
Epoch[27/50], Train Loss:37.3029, Validation Loss:33.5465 Early stopping at epoch 27. Running Exp 16: ratio=0.75, bias=128 Exp 16: Generating dataset from transform
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.01it/s]
Epoch[1/50], Train Loss:238.1733, Validation Loss:114.3996
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.18it/s]
Epoch[2/50], Train Loss:116.9242, Validation Loss:73.8845
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.25it/s]
Epoch[3/50], Train Loss:90.4713, Validation Loss:57.1701
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.61it/s]
Epoch[4/50], Train Loss:77.9621, Validation Loss:52.7388
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.18it/s]
Epoch[5/50], Train Loss:70.8742, Validation Loss:47.5035
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.18it/s]
Epoch[6/50], Train Loss:65.8898, Validation Loss:48.8457
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.35it/s]
Epoch[7/50], Train Loss:62.3547, Validation Loss:45.1048
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.38it/s]
Epoch[8/50], Train Loss:59.1296, Validation Loss:41.7984
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.48it/s]
Epoch[9/50], Train Loss:56.5841, Validation Loss:42.1535
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.17it/s]
Epoch[10/50], Train Loss:54.9483, Validation Loss:43.4511
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.24it/s]
Epoch[11/50], Train Loss:52.9686, Validation Loss:40.5861
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.94it/s]
Epoch[12/50], Train Loss:51.0351, Validation Loss:38.9018
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.30it/s]
Epoch[13/50], Train Loss:50.2271, Validation Loss:40.5351
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.25it/s]
Epoch[14/50], Train Loss:48.5910, Validation Loss:38.5479
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.27it/s]
Epoch[15/50], Train Loss:47.8392, Validation Loss:36.8478
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.58it/s]
Epoch[16/50], Train Loss:46.8486, Validation Loss:35.1079
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.10it/s]
Epoch[17/50], Train Loss:46.4057, Validation Loss:36.3022
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.46it/s]
Epoch[18/50], Train Loss:45.5190, Validation Loss:35.0780
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.47it/s]
Epoch[19/50], Train Loss:44.9061, Validation Loss:35.6568
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.14it/s]
Epoch[20/50], Train Loss:44.4391, Validation Loss:36.9888
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.33it/s]
Epoch[21/50], Train Loss:43.0484, Validation Loss:36.3367
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.26it/s]
Epoch[22/50], Train Loss:43.2141, Validation Loss:34.3796
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.72it/s]
Epoch[23/50], Train Loss:42.5940, Validation Loss:34.8551
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.44it/s]
Epoch[24/50], Train Loss:42.2033, Validation Loss:34.0473
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.57it/s]
Epoch[25/50], Train Loss:42.1040, Validation Loss:33.5987
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.11it/s]
Epoch[26/50], Train Loss:40.9533, Validation Loss:34.8460
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.68it/s]
Epoch[27/50], Train Loss:40.8190, Validation Loss:33.4179
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.45it/s]
Epoch[28/50], Train Loss:40.1469, Validation Loss:33.7978
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.24it/s]
Epoch[29/50], Train Loss:40.0832, Validation Loss:34.1435
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.22it/s]
Epoch[30/50], Train Loss:39.6368, Validation Loss:32.8258
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.55it/s]
Epoch[31/50], Train Loss:39.3748, Validation Loss:32.4090
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.68it/s]
Epoch[32/50], Train Loss:39.4121, Validation Loss:31.9992
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.18it/s]
Epoch[33/50], Train Loss:38.7223, Validation Loss:34.0902
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.48it/s]
Epoch[34/50], Train Loss:38.3510, Validation Loss:32.4099
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.10it/s]
Epoch[35/50], Train Loss:38.3619, Validation Loss:32.9930
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.41it/s]
Epoch[36/50], Train Loss:38.2448, Validation Loss:31.8528
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 15.79it/s]
Epoch[37/50], Train Loss:37.6763, Validation Loss:32.9921
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.43it/s]
Epoch[38/50], Train Loss:36.9871, Validation Loss:31.0432
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.59it/s]
Epoch[39/50], Train Loss:38.0671, Validation Loss:32.4109
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.27it/s]
Epoch[40/50], Train Loss:36.8382, Validation Loss:31.9836
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:27<00:00, 16.65it/s]
Epoch[41/50], Train Loss:37.6045, Validation Loss:32.4121
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 458/458 [00:28<00:00, 16.34it/s]
Epoch[42/50], Train Loss:36.9942, Validation Loss:31.2240 Early stopping at epoch 42.
exp3_2_loaded = torch.load("./models/exp3-2_17307070782426171.pth")
exp3_2_results = get_experiment_results(exp3_2_loaded, test_hyperparam_names=["ratio", "bias"], extra_loader=exp3_test_loader)
D:\Temps\temp\ipykernel_22900\2689293624.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
exp3_2_loaded = torch.load("./models/exp3-2_17307070782426171.pth")
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:06<00:00, 31.25it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [1.4084850590734277e-05, 0.00015363673446699977, 4.225009979563765e-05, 0.00040998580516315997, 1.6775904441601597e-05, 0.9974088072776794, 0.0017630067886784673, 3.485668639768846e-05, 8.655283636471722e-06, 0.00014804775128141046] [4.613344373183281e-08, 2.1436735551105812e-05, 0.9987912774085999, 0.0006911041564308107, 1.0980292017848114e-06, 0.0004442851641215384, 2.4895851311157458e-05, 2.0962790586054325e-05, 3.1684444365964737e-06, 1.7334310769001604e-06] [1.404105546498613e-06, 0.9989784955978394, 1.4476317119260784e-05, 2.386712822044501e-06, 1.454915945942048e-05, 7.363043152963655e-08, 1.2108579312553047e-06, 0.000983237405307591, 9.635473929847649e-08, 4.182532393315341e-06] [0.700842559337616, 0.005154207814484835, 0.024481797590851784, 0.005016915500164032, 0.005796228535473347, 0.006218563299626112, 0.0716952309012413, 0.0009235470788553357, 0.09399909526109695, 0.08587180078029633] [0.01026163436472416, 0.0030757756903767586, 0.00025122021907009184, 0.015393401496112347, 0.0022758604027330875, 0.2289666384458542, 0.7372409701347351, 0.0005035883514210582, 0.0014832924352958798, 0.0005476601072587073] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 41.46it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.0005293998401612043, 0.0007825545035302639, 0.006255303975194693, 0.06350726634263992, 0.0014771796995773911, 0.8754233717918396, 0.04659580811858177, 0.0006909003714099526, 0.0038746146019548178, 0.0008636011625640094] [0.000394436065107584, 0.00019559699285309762, 0.9118548631668091, 0.041615474969148636, 0.0007165743736550212, 0.0009087906801141798, 0.0002736233873292804, 0.0011334215523675084, 0.03999966382980347, 0.0029075529891997576] [0.008978779427707195, 0.7742469310760498, 0.010847742669284344, 0.14756830036640167, 0.0031014459673315287, 0.0008888605516403913, 0.0005936693632975221, 0.03657388314604759, 0.015144744887948036, 0.002055766759440303] [0.8220072388648987, 0.00035255486727692187, 0.010002510622143745, 0.002056727884337306, 2.704974394873716e-05, 6.215804751263931e-05, 0.05037127807736397, 6.38983110547997e-05, 0.11395983397960663, 0.0010967785492539406] [0.014801161363720894, 0.006576502695679665, 0.0033670212142169476, 0.003222749102860689, 0.00635240925475955, 0.026477931067347527, 0.9268736839294434, 0.00794184859842062, 0.0029598709661513567, 0.0014268746599555016] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:05<00:00, 40.72it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.002300459425896406, 0.009195398539304733, 0.015398573130369186, 0.029696986079216003, 0.02971511147916317, 0.5735470652580261, 0.2291260063648224, 0.02381708100438118, 0.06741079688072205, 0.019792525097727776] [1.3163851690478623e-05, 6.1848422774346545e-06, 0.9986153841018677, 0.0005884294514544308, 0.00026158918626606464, 7.750494114588946e-05, 6.78149372106418e-05, 0.00032220143475569785, 4.357180660008453e-05, 4.083500698470743e-06] [0.001602725125849247, 0.9604253768920898, 0.002502964111045003, 0.0034823580645024776, 0.0001377930457238108, 8.330817945534363e-05, 0.005184994079172611, 0.02557222917675972, 0.0008969894261099398, 0.00011132747749798] [0.7898939847946167, 0.0013752433005720377, 0.02061563730239868, 0.0014157386031001806, 0.0004938106285408139, 0.002488094149157405, 0.044465236365795135, 0.00017039837257470936, 0.010004307143390179, 0.12907756865024567] [0.08194509148597717, 0.0077087897807359695, 0.06477726250886917, 0.012072171084582806, 0.005904196295887232, 0.033564288169145584, 0.6243399381637573, 0.1513560712337494, 0.01354144886136055, 0.004790759179741144] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 43.20it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 0 0 9 1 1 8 ... First 5 pred_scores: [0.0006225058459676802, 0.004992451053112745, 0.015142451971769333, 0.01409939769655466, 0.011914124712347984, 0.9056893587112427, 0.013455850072205067, 0.027691615745425224, 0.0009359631803818047, 0.005456229206174612] [5.2076949941692874e-05, 0.0002324453234905377, 0.9970223307609558, 0.0017034785123541951, 0.00044398437603376806, 0.0002389828150626272, 7.616404036525637e-05, 8.495585643686354e-05, 2.8808593924622983e-05, 0.00011669695959426463] [0.0018049852224066854, 0.925057590007782, 0.0014245978090912104, 0.017692500725388527, 0.031432803720235825, 0.005591353867202997, 0.001352998660877347, 0.001937837223522365, 0.012121611274778843, 0.0015836323145776987] [0.9816752076148987, 0.0008569160127080977, 0.004353683907538652, 0.0002946107997559011, 0.00022513924341183156, 2.658830453583505e-05, 0.0010923953959718347, 5.229896487435326e-05, 0.00887479167431593, 0.0025483472272753716] [0.5703200697898865, 0.017371896654367447, 0.045458607375621796, 0.0011064179707318544, 0.029538340866565704, 0.0006474648253060877, 0.09900231659412384, 0.20791736245155334, 0.022870222106575966, 0.0057672918774187565] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 44.34it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [1.495090828029788e-06, 8.52965149533702e-06, 0.0002182294992962852, 0.0010385597124695778, 5.648896149068605e-06, 0.9979828596115112, 0.0005053897621110082, 0.00021224575175438076, 5.496317498909775e-06, 2.1480977011378855e-05] [1.1563573934836313e-05, 4.6002449380466715e-05, 0.9979493021965027, 0.0010337568819522858, 7.79733745730482e-05, 2.0024242985527962e-05, 4.85382042825222e-05, 0.000583082262892276, 0.00022539633209817111, 4.451701443031197e-06] [0.00018858924158848822, 0.9803897738456726, 0.0014331864658743143, 5.1308594265719876e-05, 0.014465335756540298, 0.00015431742940563709, 0.0014340149937197566, 0.00012033357779728249, 0.0016039266483858228, 0.0001591408217791468] [0.8279442191123962, 0.00012675356992986053, 0.005857052747160196, 0.00014356568863149732, 7.45604993426241e-05, 0.00010754881805041805, 0.005312909372150898, 2.2106964024715126e-05, 0.055882491171360016, 0.10452888906002045] [0.12066949158906937, 0.000940801459364593, 0.0015190566191449761, 0.000929150206502527, 2.6033549147541635e-05, 0.0015174942091107368, 0.8709896206855774, 0.002464503748342395, 0.000581133586820215, 0.0003628162376116961] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 44.10it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.0011257510632276535, 0.002324395114555955, 0.0009698372450657189, 0.008392401970922947, 0.000636860029771924, 0.9681346416473389, 0.010846104472875595, 0.0045760627835989, 0.0005479800165630877, 0.0024459746200591326] [6.584928087249864e-06, 6.854458479210734e-05, 0.9992363452911377, 0.0006563536589965224, 5.826945198350586e-06, 5.912884262215812e-06, 1.5027178790205653e-07, 5.6955805121106096e-06, 1.2100616004317999e-05, 2.48811511482927e-06] [0.0004020840860903263, 0.9979514479637146, 4.432784771779552e-05, 1.6622499970253557e-05, 0.00012082813191227615, 7.772952812956646e-06, 4.863881258643232e-05, 0.001278650015592575, 0.00011226838978473097, 1.7319811377092265e-05] [0.9855577349662781, 0.0003766135487239808, 0.0020796821918338537, 2.136012517439667e-05, 0.00016969507851172239, 3.105343785136938e-05, 0.0016751578077673912, 2.584015965112485e-05, 0.003431428223848343, 0.006631461903452873] [0.28209859132766724, 0.007852891460061073, 0.0022075348533689976, 0.00041401662747375667, 0.0005905418656766415, 0.002090037800371647, 0.6842443943023682, 0.01844847947359085, 0.0010199315147474408, 0.0010335836559534073] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 44.20it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.016115911304950714, 0.024926697835326195, 0.04051152989268303, 0.06054610759019852, 0.016367575153708458, 0.7014074921607971, 0.03293575718998909, 0.05719501152634621, 0.012037117034196854, 0.03795676678419113] [4.1752977267606184e-05, 6.956317520234734e-05, 0.9794231057167053, 0.019477836787700653, 6.37804259895347e-05, 0.00023418852651957422, 0.00010026722884504125, 0.00011816180631285533, 0.0002603048924356699, 0.00021109727094881237] [0.017268050462007523, 0.9635851383209229, 0.0008023418486118317, 0.0014263058546930552, 0.011720250360667706, 0.00014217279385775328, 0.0034987947437912226, 0.0006647322443313897, 0.0006139993201941252, 0.00027824932476505637] [0.7839810252189636, 0.003381988499313593, 0.04206053912639618, 0.02024509571492672, 0.0034175461623817682, 0.0021670679561793804, 0.015557917766273022, 0.00019963373779319227, 0.029196737334132195, 0.09979239106178284] [0.01192665845155716, 0.016705887392163277, 0.003860658733174205, 0.029717054218053818, 0.003097561653703451, 0.035155825316905975, 0.8573552966117859, 0.030598243698477745, 0.010644737631082535, 0.0009380143601447344] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 44.35it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.028106315061450005, 0.07849203050136566, 0.017998026683926582, 0.18945777416229248, 0.013153960928320885, 0.2991311252117157, 0.27383437752723694, 0.022593218833208084, 0.07084193825721741, 0.00639121700078249] [0.0004556578351184726, 0.000571919372305274, 0.9623072147369385, 0.020998740568757057, 0.005872698035091162, 0.0032054458279162645, 0.0029854984022676945, 0.0011273198761045933, 0.001450692885555327, 0.00102489092387259] [0.012423230335116386, 0.911707878112793, 0.0015081411693245173, 0.003876647213473916, 0.017411386594176292, 0.0027547962963581085, 0.03690524026751518, 0.0008730615372769535, 0.01090804673731327, 0.0016315371030941606] [0.9133642911911011, 0.011656571179628372, 0.01722148433327675, 0.002302567707374692, 0.0006077291327528656, 0.00097040826221928, 0.027017848566174507, 0.0005565431783907115, 0.0027472740039229393, 0.023555254563689232] [0.005887387786060572, 0.01069547887891531, 0.008759379386901855, 0.004854143131524324, 0.000488183053676039, 0.028830256313085556, 0.8808905482292175, 0.05849708616733551, 0.0006667506531812251, 0.00043085680226795375] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 45.68it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [4.901746433461085e-05, 0.0006404216401278973, 0.002537829801440239, 0.0011689921375364065, 0.00012017488916171715, 0.9794667959213257, 0.014431767165660858, 0.0012993161799386144, 6.487166683655232e-05, 0.00022083612566348165] [2.6164868813793873e-06, 0.0003171900170855224, 0.9739567041397095, 0.023657184094190598, 0.0013737682020291686, 8.437882934231311e-05, 5.119640991324559e-05, 0.00016419420717284083, 0.00035146618029102683, 4.1295996197732165e-05] [3.804970901910565e-06, 0.9986924529075623, 6.181386197567917e-06, 2.817822496581357e-05, 0.0006990581750869751, 8.546391472918913e-05, 0.0002468823513481766, 0.00021293330064509064, 1.9927345420001075e-05, 5.112711733090691e-06] [0.9552818536758423, 0.0014822379453107715, 0.017297152429819107, 0.0003428226336836815, 0.00011168736818945035, 1.1066692422900815e-05, 0.0004220946575514972, 2.0471214156714268e-05, 0.00559238949790597, 0.019438235089182854] [0.014782577753067017, 0.006412541028112173, 0.0013377820141613483, 0.003855399787425995, 0.0013360277516767383, 0.005272441543638706, 0.9546133875846863, 0.01006145030260086, 0.002193303080275655, 0.00013500415661837906] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 44.03it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 2 7 0 9 1 1 8 ... First 5 pred_scores: [1.3243848115962464e-05, 0.00010716387623688206, 0.00014500474208034575, 0.0011430180165916681, 5.236093329585856e-06, 0.997251570224762, 0.000352748524164781, 0.000899838691111654, 1.896104367915541e-05, 6.318630039459094e-05] [2.3472815882996656e-05, 5.9252004575682804e-05, 0.9949026107788086, 0.003567978274077177, 6.0639838920906186e-05, 1.6423853594460525e-05, 6.645655957981944e-05, 2.282139803355676e-06, 0.0012666522525250912, 3.432953599258326e-05] [0.0007859019679017365, 0.9964146614074707, 2.0030303858220577e-05, 0.0011360376374796033, 0.0008133270894177258, 1.563460136821959e-05, 4.414846625877544e-05, 0.0006704697152599692, 9.826022869674489e-05, 1.4211099141903105e-06] [0.334128737449646, 0.004807538352906704, 0.3815900683403015, 0.002176426351070404, 0.00012625244562514126, 0.001857084920629859, 0.061737142503261566, 0.0023831678554415703, 0.10443641990423203, 0.10675723850727081] [0.08477935194969177, 0.030781108886003494, 0.07328452914953232, 0.027196185663342476, 0.015664082020521164, 0.00802561640739441, 0.15211956202983856, 0.5836856961250305, 0.009008422493934631, 0.015455411747097969] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:11<00:00, 18.08it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [4.851843300457404e-07, 2.5717912649270147e-05, 3.8145000871736556e-05, 0.0003381661663297564, 8.04858973424416e-06, 0.9987831711769104, 0.0007651788182556629, 3.1822288292460144e-05, 1.1456619404270896e-06, 8.165682629623916e-06] [2.756760841293726e-05, 5.058495298726484e-05, 0.9917002320289612, 0.00793241336941719, 8.798901035333984e-06, 5.004184276913293e-05, 2.4475377358612604e-05, 4.411408008309081e-05, 0.00015389882901217788, 7.94095376477344e-06] [0.000742756761610508, 0.9349626302719116, 0.013669434934854507, 0.03873478248715401, 0.004639012273401022, 0.0017087943851947784, 4.471830470720306e-05, 0.0030083658639341593, 0.0010034401202574372, 0.0014860619558021426] [0.7886865139007568, 0.0015487547498196363, 0.002639341400936246, 0.002073105191811919, 8.190911466954276e-05, 0.0015576272271573544, 0.17271776497364044, 8.958850958151743e-05, 0.022870291024446487, 0.007735145278275013] [0.03572266548871994, 0.05743031948804855, 0.016924653202295303, 0.07449596375226974, 0.004314030986279249, 0.0380716435611248, 0.6861774325370789, 0.05483899638056755, 0.025118635967373848, 0.006905757822096348] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 41.94it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 3 2 1 0 7 0 9 1 1 8 ... First 5 pred_scores: [0.07015793025493622, 0.17115622758865356, 0.08540425449609756, 0.20495055615901947, 0.05952597036957741, 0.10878849774599075, 0.08375849574804306, 0.07079409062862396, 0.09859350323677063, 0.04687052220106125] [4.4081676605856046e-05, 4.8077818064484745e-05, 0.991249680519104, 0.005639971699565649, 0.0007752235978841782, 0.00017255399143323302, 2.9750844987574965e-05, 0.0014218427240848541, 0.00033255547168664634, 0.00028626134735532105] [0.0010905098170042038, 0.9960812926292419, 3.196727629983798e-05, 2.587202106951736e-05, 0.0017261251341551542, 2.0565807062666863e-05, 0.00019235815852880478, 0.0003763134591281414, 0.0003151794080622494, 0.00013987575948704034] [0.5256526470184326, 0.003397940658032894, 0.20172348618507385, 0.006726120598614216, 0.0003983285860158503, 0.0034677344374358654, 0.11367066949605942, 0.0014658414293080568, 0.06384214758872986, 0.07965510338544846] [0.052440475672483444, 0.043220844119787216, 0.0883965790271759, 0.008692672476172447, 0.08061914145946503, 0.02406121790409088, 0.30470046401023865, 0.37336692214012146, 0.001194905024021864, 0.02330685406923294] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 42.05it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [3.597833256208105e-06, 6.438692071242258e-05, 0.0002519314002711326, 0.0017037951620295644, 4.2037128878291696e-05, 0.9945317506790161, 0.0005031924229115248, 0.002642518375068903, 1.5962374163791537e-05, 0.00024082601885311306] [7.779494626447558e-05, 0.0006553674465976655, 0.9472787380218506, 0.047815173864364624, 0.0002373058523517102, 3.6603182707040105e-06, 7.803495100233704e-05, 0.0011360173812136054, 0.002616874873638153, 0.00010097779158968478] [4.374469062895514e-05, 0.9996809959411621, 4.769039492202864e-07, 3.409303099033423e-05, 0.0001126454517361708, 2.482489662725129e-06, 6.439723847506684e-07, 8.02894719527103e-05, 2.995315117004793e-05, 1.4705059584230185e-05] [0.9645154476165771, 0.00026416091714054346, 0.0248175710439682, 3.558121170499362e-05, 3.673982064356096e-05, 2.178904651373159e-06, 0.003810783615335822, 0.000319961691275239, 0.0004410597321111709, 0.0057565295137465] [0.014911935664713383, 0.003560146549716592, 0.0006212932639755309, 0.0007772716344334185, 0.00020597100956365466, 0.009275960735976696, 0.9555924534797668, 0.014598730020225048, 0.00035614726948551834, 0.00010009680408984423] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 43.23it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 7 0 9 1 1 8 ... First 5 pred_scores: [3.2412588097940898e-06, 0.00013902496721129864, 0.000850516022183001, 0.004183612298220396, 3.624974488047883e-05, 0.9898215532302856, 0.004359999671578407, 0.00027691904688254, 0.00022434871061705053, 0.00010461898637004197] [0.00022298125259112567, 0.0005470126052387059, 0.9858696460723877, 0.006286725867539644, 0.002832188969478011, 0.00011601192090893164, 0.00017208665667567402, 0.00012192568829050288, 0.0033474795054644346, 0.00048405127017758787] [0.0023837173357605934, 0.9886946082115173, 0.00019612832693383098, 0.0007128281285986304, 0.0022844187915325165, 0.00021370529429987073, 0.002416681032627821, 0.0010541967349126935, 0.0017658909782767296, 0.00027792321634478867] [0.93978351354599, 0.0008927106973715127, 0.004815163556486368, 0.0011128607438877225, 3.4890406823251396e-05, 0.0006405484746210277, 0.02138824574649334, 9.770592441782355e-06, 0.019156185910105705, 0.012166231870651245] [0.12144490331411362, 0.16882486641407013, 0.004181669093668461, 0.14566218852996826, 0.012995201162993908, 0.03214992210268974, 0.24232794344425201, 0.2490014284849167, 0.02243873104453087, 0.0009731113677844405] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 42.89it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.009119640104472637, 0.06536643207073212, 0.006831802427768707, 0.07327910512685776, 0.01973419077694416, 0.667778491973877, 0.06956251710653305, 0.03421767055988312, 0.015636621043086052, 0.03847353160381317] [0.00040858390275388956, 0.0009829828049987555, 0.9308865666389465, 0.029090067371726036, 0.01683911494910717, 0.0028024534694850445, 0.0030853417702019215, 0.0077262455597519875, 0.004008439835160971, 0.0041702608577907085] [0.0003964568895753473, 0.9537023901939392, 0.0012553994311019778, 0.007468781899660826, 0.022855613380670547, 0.003612005850300193, 0.0022762741427868605, 0.0058357445523142815, 0.0015966200735419989, 0.0010006665252149105] [0.9443862438201904, 0.0027761405799537897, 0.003673307364806533, 0.0006113144336268306, 0.0024107180070132017, 0.0007488335249945521, 0.02578374557197094, 0.00021683305385522544, 0.006610645446926355, 0.012782242149114609] [0.042677804827690125, 0.0014312515268102288, 0.00028432553517632186, 0.00039916642708703876, 0.00030553725082427263, 0.002235010964795947, 0.9517112970352173, 0.0008258689194917679, 8.420299127465114e-05, 4.543368049780838e-05] ...
100%|āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā| 204/204 [00:04<00:00, 42.45it/s]
First 10 true labels: 5 2 1 0 6 1 9 1 1 8 ... First 10 pred labels: 5 2 1 0 6 0 9 1 1 8 ... First 5 pred_scores: [0.003135780803859234, 0.012197917327284813, 0.00956991408020258, 0.01713808998465538, 0.0025515828747302294, 0.8907844424247742, 0.03306581452488899, 0.003669464262202382, 0.016395481303334236, 0.011491495184600353] [0.0016964542446658015, 0.0001722887682262808, 0.9916563034057617, 0.0027529054787009954, 0.0024818682577461004, 1.8085218471242115e-05, 5.506380330189131e-05, 0.0008463860140182078, 9.126665099756792e-05, 0.00022943907242733985] [0.002470254898071289, 0.986940324306488, 0.0004844145732931793, 0.0031957230530679226, 0.0034528577234596014, 0.000288346316665411, 7.99939371063374e-05, 0.001757446676492691, 0.000542913971003145, 0.0007877147872932255] [0.8563277721405029, 0.002169413957744837, 0.04591339826583862, 0.0009987354278564453, 0.0011445622658357024, 0.0003237711498513818, 0.0080312080681324, 0.00036918619298376143, 0.012516087852418423, 0.07220588624477386] [0.007392268627882004, 0.013712205924093723, 0.011281101033091545, 0.006759864278137684, 0.0024448635522276163, 0.009955089539289474, 0.9174121022224426, 0.014565134420990944, 0.015555341728031635, 0.0009220243664458394] ...
plot_el(exp3_2_loaded, ["ratio", "bias"], n_rows=4, n_cols=4)
plot_cm(exp3_2_results, ["ratio", "bias"], n_rows=4, n_cols=4)
exp3_2_accuracies, exp3_2_f1s = plot_pr(exp3_2_results, ["ratio", "bias"], n_rows=4, n_cols=4)
print_metrics(exp3_2_accuracies, exp3_2_f1s)
Accuracies: 0.924 0.912 0.877 0.885 0.925 0.911 0.913 0.830 0.924 0.913 0.908 0.808 0.928 0.898 0.861 0.852 F1 Score Lists: 0.916 0.949 0.949 0.902 0.943 0.928 0.892 0.928 0.861 0.883 | Avg F1=0.915, Std F1=0.028178712032797315 0.910 0.938 0.938 0.866 0.932 0.924 0.898 0.914 0.856 0.873 | Avg F1=0.905, Std F1=0.02901513509491587 0.859 0.902 0.923 0.845 0.863 0.896 0.843 0.888 0.809 0.838 | Avg F1=0.867, Std F1=0.03323173297079609 0.864 0.914 0.926 0.856 0.888 0.888 0.854 0.890 0.825 0.844 | Avg F1=0.875, Std F1=0.030212915176477208 0.918 0.941 0.949 0.909 0.945 0.931 0.902 0.921 0.875 0.891 | Avg F1=0.918, Std F1=0.02315118767203162 0.890 0.933 0.936 0.887 0.931 0.924 0.899 0.900 0.854 0.875 | Avg F1=0.903, Std F1=0.02627288161038198 0.905 0.935 0.940 0.881 0.934 0.919 0.890 0.919 0.870 0.873 | Avg F1=0.907, Std F1=0.02514265979976644 0.774 0.862 0.885 0.806 0.838 0.865 0.796 0.820 0.738 0.785 | Avg F1=0.817, Std F1=0.04367929965531259 0.930 0.944 0.947 0.904 0.933 0.922 0.910 0.918 0.884 0.883 | Avg F1=0.917, Std F1=0.021337897218826583 0.883 0.938 0.937 0.884 0.947 0.920 0.898 0.916 0.868 0.855 | Avg F1=0.904, Std F1=0.03000580711454836 0.896 0.932 0.937 0.874 0.930 0.916 0.893 0.906 0.853 0.868 | Avg F1=0.901, Std F1=0.027517225325711638 0.787 0.821 0.879 0.762 0.819 0.814 0.790 0.825 0.720 0.747 | Avg F1=0.796, Std F1=0.04325151448553455 0.929 0.951 0.947 0.908 0.945 0.934 0.907 0.923 0.880 0.889 | Avg F1=0.922, Std F1=0.023251275786566786 0.895 0.931 0.921 0.872 0.907 0.901 0.865 0.901 0.839 0.861 | Avg F1=0.889, Std F1=0.027568983031421735 0.835 0.885 0.919 0.818 0.873 0.882 0.836 0.861 0.766 0.801 | Avg F1=0.848, Std F1=0.04310050816257488 0.809 0.875 0.914 0.806 0.859 0.880 0.842 0.825 0.780 0.813 | Avg F1=0.841, Std F1=0.03902650406749625 Best: 13-th
plot_rocauc(exp3_2_results, ["ratio", "bias"], curve_type="all", n_rows=4, n_cols=4)
plot_rocauc(exp3_2_results, ["ratio", "bias"], curve_type="macro_micro", n_rows=4, n_cols=4)
def exp3_2_peek(dataset, hyper_params, index=21642, ratio=1.0, bias=0):
exp3_train_dataset_sample = dataset.overwrite(range(exp3_train_dataset.__len__())) # Deep copy of original
temp_mean, temp_std = exp3_train_dataset_sample.get_meanstd(bias=bias)
exp3_train_dataset_sample.transform = A.Compose([
A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
A.RandomResizedCrop(32, 32, scale=(hyper_params["crop"], 1.0), ratio=(ratio, 1.0 / ratio)),
A.Rotate(limit=hyper_params["angle"]),
A.Normalize(mean=temp_mean, std=temp_std),
ToTensorV2()
])
peek(exp3_train_dataset_sample, index=index)
del exp3_train_dataset_sample, temp_mean, temp_std
exp3_2_peek(exp3_train_dataset, exp3_2_hyperparams, index=57468, ratio=0.75, bias=0)
D:\Temps\temp\ipykernel_22900\259240058.py:5: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial(). A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
Peeking data from training set of index 57468. Image Tnesor Size:torch.Size([3, 32, 32])
exp3_2_peek(exp3_train_dataset, exp3_2_hyperparams, index=57468, ratio=0.75, bias=32)
D:\Temps\temp\ipykernel_22900\259240058.py:5: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial(). A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
Peeking data from training set of index 57468. Image Tnesor Size:torch.Size([3, 32, 32])
exp3_2_peek(exp3_train_dataset, exp3_2_hyperparams, index=57468, ratio=0.75, bias=64)
D:\Temps\temp\ipykernel_22900\259240058.py:5: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial(). A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
Peeking data from training set of index 57468. Image Tnesor Size:torch.Size([3, 32, 32])
exp3_2_peek(exp3_train_dataset, exp3_2_hyperparams, index=57468, ratio=0.75, bias=128)
D:\Temps\temp\ipykernel_22900\259240058.py:5: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial(). A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
Peeking data from training set of index 57468. Image Tnesor Size:torch.Size([3, 32, 32])